{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9be5307c-2ed7-43fe-9a5d-6ea43a038a5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 bda8da72 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete ✅ (128 CPUs, 1007.5 GB RAM, 18.5/30.0 GB disk)\n"
     ]
    }
   ],
   "source": [
    "import comet_ml\n",
    "import torch\n",
    "import utils\n",
    "\n",
    "comet_ml.init(project_name='exp_100epoch')\n",
    "# 这里应该会包含100epoch的0,0.6,1.2加雾以及各个以100epoch为单位的增量\n",
    "display = utils.notebook_init()  # checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fcf77c98-93b2-4ddb-95dc-3e13865ba015",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "from fog_test.for_different_strength import mix_dataset\n",
    "origin_ratio = {\n",
    "    '1.0':1,\n",
    "}\n",
    "# 先初始化数据集训练一个没有家务数据的\n",
    "mix_dataset(fogged_folder = '../datasets/fogged/', \n",
    "            ratio = origin_ratio,\n",
    "            train_folder = '../datasets/kitti/images/origin_train', \n",
    "            out_folder = '../datasets/kitti/images/train'\n",
    "               )\n",
    "\n",
    "val_fogged_strength = 1.0\n",
    "# 替换验证集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/val/* &&\\\n",
    "cp /root/autodl-tmp/datasets/fogged/val_fogged_strength{val_fogged_strength}/* ../datasets/kitti/images/val/ && \\\n",
    "echo 'Val set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52533de7-a4b8-4317-aa09-c698dc78b9ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "freeze_layer = ' '.join([str(i) for i in range(24)]) # 冻结0 ~ 23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e76cd7b2-0ff9-49f6-9c18-c843b6f5281a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=fog_0_to_fog_1.0_freeze_0-23, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 0f1025d8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "fatal: unable to access 'https://github.com/ultralytics/yolov5/': GnuTLS recv error (-110): The TLS connection was non-properly terminated.\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/5bc3cec77061498f9240af478f6e9150\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "freezing model.0.conv.weight\n",
      "freezing model.0.bn.weight\n",
      "freezing model.0.bn.bias\n",
      "freezing model.1.conv.weight\n",
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      "freezing model.2.cv3.conv.weight\n",
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      "freezing model.9.cv1.conv.weight\n",
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      "freezing model.13.cv1.conv.weight\n",
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      "freezing model.13.cv3.conv.weight\n",
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      "freezing model.13.m.0.cv1.conv.weight\n",
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      "freezing model.14.conv.weight\n",
      "freezing model.14.bn.weight\n",
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      "freezing model.17.cv1.conv.weight\n",
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      "freezing model.17.cv2.conv.weight\n",
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      "freezing model.17.cv3.conv.weight\n",
      "freezing model.17.cv3.bn.weight\n",
      "freezing model.17.cv3.bn.bias\n",
      "freezing model.17.m.0.cv1.conv.weight\n",
      "freezing model.17.m.0.cv1.bn.weight\n",
      "freezing model.17.m.0.cv1.bn.bias\n",
      "freezing model.17.m.0.cv2.conv.weight\n",
      "freezing model.17.m.0.cv2.bn.weight\n",
      "freezing model.17.m.0.cv2.bn.bias\n",
      "freezing model.18.conv.weight\n",
      "freezing model.18.bn.weight\n",
      "freezing model.18.bn.bias\n",
      "freezing model.20.cv1.conv.weight\n",
      "freezing model.20.cv1.bn.weight\n",
      "freezing model.20.cv1.bn.bias\n",
      "freezing model.20.cv2.conv.weight\n",
      "freezing model.20.cv2.bn.weight\n",
      "freezing model.20.cv2.bn.bias\n",
      "freezing model.20.cv3.conv.weight\n",
      "freezing model.20.cv3.bn.weight\n",
      "freezing model.20.cv3.bn.bias\n",
      "freezing model.20.m.0.cv1.conv.weight\n",
      "freezing model.20.m.0.cv1.bn.weight\n",
      "freezing model.20.m.0.cv1.bn.bias\n",
      "freezing model.20.m.0.cv2.conv.weight\n",
      "freezing model.20.m.0.cv2.bn.weight\n",
      "freezing model.20.m.0.cv2.bn.bias\n",
      "freezing model.21.conv.weight\n",
      "freezing model.21.bn.weight\n",
      "freezing model.21.bn.bias\n",
      "freezing model.23.cv1.conv.weight\n",
      "freezing model.23.cv1.bn.weight\n",
      "freezing model.23.cv1.bn.bias\n",
      "freezing model.23.cv2.conv.weight\n",
      "freezing model.23.cv2.bn.weight\n",
      "freezing model.23.cv2.bn.bias\n",
      "freezing model.23.cv3.conv.weight\n",
      "freezing model.23.cv3.bn.weight\n",
      "freezing model.23.cv3.bn.bias\n",
      "freezing model.23.m.0.cv1.conv.weight\n",
      "freezing model.23.m.0.cv1.bn.weight\n",
      "freezing model.23.m.0.cv1.bn.bias\n",
      "freezing model.23.m.0.cv2.conv.weight\n",
      "freezing model.23.m.0.cv2.bn.weight\n",
      "freezing model.23.m.0.cv2.bn.bias\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train... 4189 images, 0 b\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/train.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/fog_0_to_fog_1.0_freeze_0-23/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/fog_0_to_fog_1.0_freeze_0-23\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      1.01G    0.04781    0.06523    0.01605        128        640: 1\n",
      "tensor([1.61357], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.79      0.412       0.48      0.288\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      1.02G    0.04846    0.05175    0.01526        133        640: 1\n",
      "tensor([1.66685], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.711      0.457      0.496      0.285\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      1.02G    0.05136     0.0473    0.01502        131        640: 1\n",
      "tensor([1.52828], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.747      0.464      0.517      0.284\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      1.02G    0.05279    0.04579    0.01361        108        640: 1\n",
      "tensor([1.25203], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.745      0.476      0.522      0.299\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      1.02G    0.05238    0.04532    0.01314        156        640: 1\n",
      "tensor([1.38679], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.745      0.462      0.515       0.29\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      1.02G    0.05251    0.04502    0.01281        123        640: 1\n",
      "tensor([1.33683], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.772      0.456      0.513      0.282\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      1.02G    0.05244    0.04468    0.01288        174        640: 1\n",
      "tensor([1.57973], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.696       0.47      0.504       0.28\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      1.02G    0.05205    0.04392    0.01252        166        640: 1\n",
      "tensor([1.62184], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.75      0.462      0.522        0.3\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      1.02G    0.05169    0.04436    0.01235        152        640: 1\n",
      "tensor([1.42571], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.725      0.482      0.525      0.287\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      1.02G    0.05216    0.04454    0.01286        136        640: 1\n",
      "tensor([1.34103], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.753       0.47      0.528      0.297\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      1.02G    0.05195    0.04445    0.01243        134        640: 1\n",
      "tensor([1.47632], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.75      0.459      0.509      0.285\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      1.02G    0.05192    0.04421    0.01249        182        640: 1\n",
      "tensor([1.43977], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.734      0.482      0.523      0.298\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      1.02G    0.05175    0.04433    0.01231        128        640: 1\n",
      "tensor([1.26924], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.735      0.475      0.511      0.289\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      1.02G    0.05131    0.04386    0.01241        112        640: 1\n",
      "tensor([1.53586], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.757      0.466      0.529        0.3\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      1.02G    0.05082    0.04433    0.01233        151        640: 1\n",
      "tensor([1.19854], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.755      0.459      0.518      0.296\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      1.02G    0.05145    0.04449    0.01249        132        640: 1\n",
      "tensor([1.43136], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.722      0.467       0.51      0.292\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      1.02G    0.05095    0.04398    0.01246        131        640: 1\n",
      "tensor([1.32701], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.755      0.469      0.527      0.302\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      1.02G    0.05104    0.04403    0.01221        159        640: 1\n",
      "tensor([1.47992], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.722      0.469      0.509      0.291\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      1.02G     0.0504    0.04391    0.01232        125        640: 1\n",
      "tensor([1.38739], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.712      0.482       0.51      0.293\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      1.02G    0.05114     0.0446    0.01254         88        640: 1\n",
      "tensor([1.16510], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.774      0.463      0.522      0.296\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      1.02G    0.05106     0.0435    0.01245        137        640: 1\n",
      "tensor([1.63082], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.78      0.466      0.527        0.3\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      1.02G    0.05091    0.04441    0.01224        166        640: 1\n",
      "tensor([1.51553], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.751      0.463      0.525      0.297\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      1.02G    0.05022    0.04405    0.01227        161        640: 1\n",
      "tensor([1.48232], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.762      0.471       0.53      0.304\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      1.02G    0.05024    0.04381    0.01236        118        640: 1\n",
      "tensor([1.36060], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.756      0.465      0.524        0.3\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      1.02G    0.05006    0.04377    0.01214        151        640: 1\n",
      "tensor([1.41004], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.775      0.463      0.525      0.304\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      1.02G    0.04981    0.04438    0.01234        133        640: 1\n",
      "tensor([1.37590], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.783      0.466      0.531      0.301\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      1.02G     0.0501    0.04384    0.01213        154        640: 1\n",
      "tensor([1.60689], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.766      0.463       0.53      0.299\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      1.02G    0.04974    0.04418    0.01232        122        640: 1\n",
      "tensor([1.19515], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.777      0.465      0.527      0.306\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      1.02G    0.04979    0.04415    0.01195        123        640: 1\n",
      "tensor([1.22952], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.778      0.468      0.532      0.299\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      1.02G    0.04996     0.0438    0.01212        127        640: 1\n",
      "tensor([1.24069], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.769      0.458      0.521        0.3\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      1.02G    0.04984    0.04263    0.01215        127        640: 1\n",
      "tensor([1.27212], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.762      0.472       0.53      0.303\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      1.02G    0.04937    0.04365    0.01198        122        640: 1\n",
      "tensor([1.38815], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.767      0.462      0.524      0.294\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      1.02G    0.04954    0.04389    0.01206        146        640: 1\n",
      "tensor([1.40857], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.774      0.465      0.528      0.299\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      1.02G    0.04927    0.04327    0.01198        202        640: 1\n",
      "tensor([1.60850], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.765      0.466      0.533      0.302\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      1.02G    0.04938     0.0434    0.01228         94        640: 1\n",
      "tensor([1.18591], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.788      0.464      0.532      0.304\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      1.02G     0.0492     0.0435    0.01217        152        640: 1\n",
      "tensor([1.61630], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.777      0.466      0.526      0.307\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      1.02G    0.04897    0.04361    0.01243        123        640: 1\n",
      "tensor([1.17516], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.774      0.461      0.527      0.304\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      1.02G    0.04925    0.04368    0.01237        162        640: 1\n",
      "tensor([1.45115], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.763      0.466      0.529       0.31\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      1.02G    0.04906    0.04393     0.0123        161        640: 1\n",
      "tensor([1.26761], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.788      0.462       0.53       0.31\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      1.02G    0.04873     0.0439     0.0121        122        640: 1\n",
      "tensor([1.32223], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   ^C\n",
      "                 Class     Images  Instances          P          R      mAP50   \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--name fog_0_to_fog_1.0_freeze_0-23 \\\n",
    "--freeze {freeze_layer} \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8229f8a1-8553-4fe0-93f1-5dab3b55894d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/fog_0_to_fog_1.0_freeze_0-23/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 bda8da72 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.589      0.247      0.243       0.12\n",
      "                   Car       2244       8711      0.699      0.604      0.635      0.335\n",
      "                   Van       2244        861      0.325      0.332       0.28      0.149\n",
      "                 Truck       2244        333      0.243      0.414      0.239      0.114\n",
      "                  Tram       2244        138      0.841     0.0769      0.159     0.0755\n",
      "            Pedestrian       2244       1286      0.509      0.389      0.358      0.149\n",
      "        Person_sitting       2244         89      0.321     0.0337     0.0449     0.0204\n",
      "               Cyclist       2244        496          1    0.00737     0.0642     0.0239\n",
      "                  Misc       2244        284      0.773       0.12      0.163     0.0922\n",
      "Speed: 0.1ms pre-process, 1.1ms inference, 1.2ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp89\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾测试集\n",
    "model = f'runs/train/fog_0_to_fog_1.0_freeze_0-23/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d760afe-6ed8-47cf-a279-39f7875d296a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bc3ab6f-ed4a-4334-b4b3-d8298e0470a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "049e4c4d-7810-463b-b97a-e17e2394e7c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2030c8e8-6a99-4c52-922a-b4dbd8c4d5ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f9897053-994f-4895-808b-2a423335dec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dont_freeze = [13, 17, 20, 23, 24]\n",
    "freeze_layer = ' '.join([str(i) for i in range(25) if i not in dont_freeze]) # 冻结0 ~ 23\n",
    "dont_freeze_str = '_'.join([str(i) for i in dont_freeze])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e682b924-a151-4a74-a110-7417434aed66",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=not_freeze:13_17_20_23_24, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 19, 21, 22], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 0f1025d8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/841143a6527d4587a08a8dbe7f697f44\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "freezing model.0.conv.weight\n",
      "freezing model.0.bn.weight\n",
      "freezing model.0.bn.bias\n",
      "freezing model.1.conv.weight\n",
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      "freezing model.2.cv1.conv.weight\n",
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      "freezing model.2.cv1.bn.bias\n",
      "freezing model.2.cv2.conv.weight\n",
      "freezing model.2.cv2.bn.weight\n",
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      "freezing model.2.cv3.conv.weight\n",
      "freezing model.2.cv3.bn.weight\n",
      "freezing model.2.cv3.bn.bias\n",
      "freezing model.2.m.0.cv1.conv.weight\n",
      "freezing model.2.m.0.cv1.bn.weight\n",
      "freezing model.2.m.0.cv1.bn.bias\n",
      "freezing model.2.m.0.cv2.conv.weight\n",
      "freezing model.2.m.0.cv2.bn.weight\n",
      "freezing model.2.m.0.cv2.bn.bias\n",
      "freezing model.3.conv.weight\n",
      "freezing model.3.bn.weight\n",
      "freezing model.3.bn.bias\n",
      "freezing model.4.cv1.conv.weight\n",
      "freezing model.4.cv1.bn.weight\n",
      "freezing model.4.cv1.bn.bias\n",
      "freezing model.4.cv2.conv.weight\n",
      "freezing model.4.cv2.bn.weight\n",
      "freezing model.4.cv2.bn.bias\n",
      "freezing model.4.cv3.conv.weight\n",
      "freezing model.4.cv3.bn.weight\n",
      "freezing model.4.cv3.bn.bias\n",
      "freezing model.4.m.0.cv1.conv.weight\n",
      "freezing model.4.m.0.cv1.bn.weight\n",
      "freezing model.4.m.0.cv1.bn.bias\n",
      "freezing model.4.m.0.cv2.conv.weight\n",
      "freezing model.4.m.0.cv2.bn.weight\n",
      "freezing model.4.m.0.cv2.bn.bias\n",
      "freezing model.4.m.1.cv1.conv.weight\n",
      "freezing model.4.m.1.cv1.bn.weight\n",
      "freezing model.4.m.1.cv1.bn.bias\n",
      "freezing model.4.m.1.cv2.conv.weight\n",
      "freezing model.4.m.1.cv2.bn.weight\n",
      "freezing model.4.m.1.cv2.bn.bias\n",
      "freezing model.5.conv.weight\n",
      "freezing model.5.bn.weight\n",
      "freezing model.5.bn.bias\n",
      "freezing model.6.cv1.conv.weight\n",
      "freezing model.6.cv1.bn.weight\n",
      "freezing model.6.cv1.bn.bias\n",
      "freezing model.6.cv2.conv.weight\n",
      "freezing model.6.cv2.bn.weight\n",
      "freezing model.6.cv2.bn.bias\n",
      "freezing model.6.cv3.conv.weight\n",
      "freezing model.6.cv3.bn.weight\n",
      "freezing model.6.cv3.bn.bias\n",
      "freezing model.6.m.0.cv1.conv.weight\n",
      "freezing model.6.m.0.cv1.bn.weight\n",
      "freezing model.6.m.0.cv1.bn.bias\n",
      "freezing model.6.m.0.cv2.conv.weight\n",
      "freezing model.6.m.0.cv2.bn.weight\n",
      "freezing model.6.m.0.cv2.bn.bias\n",
      "freezing model.6.m.1.cv1.conv.weight\n",
      "freezing model.6.m.1.cv1.bn.weight\n",
      "freezing model.6.m.1.cv1.bn.bias\n",
      "freezing model.6.m.1.cv2.conv.weight\n",
      "freezing model.6.m.1.cv2.bn.weight\n",
      "freezing model.6.m.1.cv2.bn.bias\n",
      "freezing model.6.m.2.cv1.conv.weight\n",
      "freezing model.6.m.2.cv1.bn.weight\n",
      "freezing model.6.m.2.cv1.bn.bias\n",
      "freezing model.6.m.2.cv2.conv.weight\n",
      "freezing model.6.m.2.cv2.bn.weight\n",
      "freezing model.6.m.2.cv2.bn.bias\n",
      "freezing model.7.conv.weight\n",
      "freezing model.7.bn.weight\n",
      "freezing model.7.bn.bias\n",
      "freezing model.8.cv1.conv.weight\n",
      "freezing model.8.cv1.bn.weight\n",
      "freezing model.8.cv1.bn.bias\n",
      "freezing model.8.cv2.conv.weight\n",
      "freezing model.8.cv2.bn.weight\n",
      "freezing model.8.cv2.bn.bias\n",
      "freezing model.8.cv3.conv.weight\n",
      "freezing model.8.cv3.bn.weight\n",
      "freezing model.8.cv3.bn.bias\n",
      "freezing model.8.m.0.cv1.conv.weight\n",
      "freezing model.8.m.0.cv1.bn.weight\n",
      "freezing model.8.m.0.cv1.bn.bias\n",
      "freezing model.8.m.0.cv2.conv.weight\n",
      "freezing model.8.m.0.cv2.bn.weight\n",
      "freezing model.8.m.0.cv2.bn.bias\n",
      "freezing model.9.cv1.conv.weight\n",
      "freezing model.9.cv1.bn.weight\n",
      "freezing model.9.cv1.bn.bias\n",
      "freezing model.9.cv2.conv.weight\n",
      "freezing model.9.cv2.bn.weight\n",
      "freezing model.9.cv2.bn.bias\n",
      "freezing model.10.conv.weight\n",
      "freezing model.10.bn.weight\n",
      "freezing model.10.bn.bias\n",
      "freezing model.14.conv.weight\n",
      "freezing model.14.bn.weight\n",
      "freezing model.14.bn.bias\n",
      "freezing model.18.conv.weight\n",
      "freezing model.18.bn.weight\n",
      "freezing model.18.bn.bias\n",
      "freezing model.21.conv.weight\n",
      "freezing model.21.bn.weight\n",
      "freezing model.21.bn.bias\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/not_freeze:13_17_20_23_24/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/not_freeze:13_17_20_23_24\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      1.47G    0.04157    0.04107    0.01215        128        640: 1\n",
      "tensor([1.12953], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.782      0.574       0.64      0.387\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      1.47G    0.03919    0.03564   0.009445        133        640: 1\n",
      "tensor([1.18018], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.771      0.606      0.678      0.401\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      1.47G    0.03995    0.03545   0.008947        131        640: 1\n",
      "tensor([1.06547], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.817      0.596      0.699      0.404\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      1.47G    0.03979    0.03519   0.008314        108        640: 1\n",
      "tensor([0.88902], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.828      0.621      0.713      0.411\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      1.47G    0.03911    0.03447   0.007815        156        640: 1\n",
      "tensor([1.01881], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.833      0.634      0.723      0.422\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      1.47G    0.03825    0.03405   0.007511        123        640: 1\n",
      "tensor([0.95429], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.762       0.65      0.719      0.425\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      1.47G      0.038    0.03364   0.007275        174        640: 1\n",
      "tensor([1.16411], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.843       0.63      0.731      0.421\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      1.47G    0.03746    0.03298   0.006989        166        640: 1\n",
      "tensor([1.18658], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.837      0.637      0.735      0.429\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      1.47G    0.03741    0.03325   0.006719        152        640: 1\n",
      "tensor([1.00798], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.817       0.66      0.741      0.436\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      1.47G    0.03733    0.03335   0.006811        136        640: 1\n",
      "tensor([0.99042], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.838      0.657      0.743      0.439\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      1.47G    0.03696    0.03314   0.006553        134        640: 1\n",
      "tensor([0.97653], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.782      0.682      0.745      0.443\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      1.47G    0.03667    0.03263   0.006498        182        640: 1\n",
      "tensor([1.06951], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.815      0.681      0.761      0.442\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      1.47G    0.03672    0.03289   0.006352        128        640: 1\n",
      "tensor([0.88281], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.846      0.649      0.742      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      1.47G     0.0364    0.03234   0.006266        112        640: 1\n",
      "tensor([1.03567], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.847      0.685      0.771      0.455\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      1.47G    0.03634     0.0326   0.006169        151        640: 1\n",
      "tensor([0.90270], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.834      0.656      0.769      0.465\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      1.47G    0.03617    0.03267   0.006151        132        640: 1\n",
      "tensor([0.95649], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868      0.663      0.773      0.469\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      1.47G    0.03593    0.03224   0.006008        131        640: 1\n",
      "tensor([0.88649], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.85      0.655      0.764      0.459\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      1.47G    0.03618    0.03231   0.005983        159        640: 1\n",
      "tensor([1.04619], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868      0.674      0.777      0.473\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      1.47G    0.03585    0.03206   0.005887        125        640: 1\n",
      "tensor([0.90463], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.812      0.697      0.773      0.469\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      1.47G    0.03571     0.0323   0.006027         88        640: 1\n",
      "tensor([0.77160], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.862      0.652      0.775      0.473\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      1.47G    0.03553    0.03147   0.005775        137        640: 1\n",
      "tensor([1.11751], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.826      0.679      0.771      0.475\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      1.47G    0.03537    0.03216   0.005615        166        640: 1\n",
      "tensor([1.04647], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.83      0.698      0.779      0.487\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      1.47G    0.03516    0.03184   0.005648        161        640: 1\n",
      "tensor([1.02667], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.806      0.712      0.777      0.475\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      1.47G    0.03529    0.03156    0.00551        118        640: 1\n",
      "tensor([0.86774], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.86      0.695      0.787      0.479\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      1.47G    0.03508    0.03163   0.005622        151        640: 1\n",
      "tensor([1.02135], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.848       0.69       0.78      0.474\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      1.47G    0.03494    0.03183   0.005479        133        640: 1\n",
      "tensor([0.90713], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.827      0.696      0.776      0.483\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      1.47G    0.03487    0.03153   0.005476        154        640: 1\n",
      "tensor([1.06687], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.876      0.668      0.783      0.494\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      1.47G    0.03483    0.03166    0.00554        122        640: 1\n",
      "tensor([0.85471], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.829      0.697      0.788      0.489\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      1.47G    0.03476    0.03154   0.005149        123        640: 1\n",
      "tensor([0.78136], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.853      0.692      0.783      0.484\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      1.47G    0.03473    0.03133   0.005284        127        640: 1\n",
      "tensor([0.81142], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.862       0.69      0.784      0.489\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      1.47G    0.03458    0.03037   0.005236        127        640: 1\n",
      "tensor([0.79705], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.698      0.783       0.49\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      1.47G    0.03444    0.03101   0.005214        122        640: 1\n",
      "tensor([0.92826], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.677      0.787      0.492\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      1.47G    0.03453    0.03134   0.005131        146        640: 1\n",
      "tensor([0.96953], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.849      0.708      0.792       0.49\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      1.47G    0.03426     0.0308   0.005054        202        640: 1\n",
      "tensor([1.08354], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.873      0.686      0.793      0.496\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      1.47G    0.03444    0.03078   0.005089         94        640: 1\n",
      "tensor([0.71321], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.848      0.713      0.798      0.494\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      1.47G    0.03418    0.03075   0.005034        152        640: 1\n",
      "tensor([1.05298], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.697      0.797        0.5\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      1.47G    0.03411    0.03073   0.005137        123        640: 1\n",
      "tensor([0.81802], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.869      0.701      0.794      0.494\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      1.47G    0.03399    0.03082   0.005136        162        640: 1\n",
      "tensor([0.90823], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874      0.702      0.792        0.5\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      1.47G    0.03419    0.03111   0.005102        161        640: 1\n",
      "tensor([0.90610], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856      0.709      0.797      0.502\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      1.47G    0.03397    0.03098   0.004936        122        640: 1\n",
      "tensor([0.84139], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.863        0.7      0.791      0.494\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      1.47G    0.03394    0.03061   0.005139        126        640: 1\n",
      "tensor([0.78466], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.705      0.802      0.504\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      1.47G    0.03373    0.03065   0.004868         90        640: 1\n",
      "tensor([0.75443], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.862      0.712        0.8      0.505\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      1.47G    0.03364    0.03059    0.00484        118        640: 1\n",
      "tensor([0.88674], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.847      0.713      0.799      0.506\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      1.47G    0.03364    0.03066   0.004724        157        640: 1\n",
      "tensor([0.93855], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.806      0.742      0.798      0.506\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      1.47G    0.03365    0.03071   0.004673        104        640: 1\n",
      "tensor([0.67519], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.862      0.724      0.809      0.512\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      1.47G    0.03358    0.03106   0.004663        157        640: 1\n",
      "tensor([0.91646], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.86      0.716      0.808      0.515\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      1.47G    0.03351    0.03034   0.004564        108        640: 1\n",
      "tensor([0.76465], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866      0.726      0.802      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      1.47G    0.03363     0.0305   0.004838        159        640: 1\n",
      "tensor([0.91625], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.845      0.719      0.794      0.504\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      1.47G    0.03317    0.03035    0.00462        118        640: 1\n",
      "tensor([0.88782], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874      0.696      0.796      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      1.47G    0.03332    0.03077   0.004642        176        640: 1\n",
      "tensor([1.05469], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.719       0.81      0.508\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      1.47G    0.03347    0.03038   0.004726        130        640: 1\n",
      "tensor([0.83449], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.852      0.731       0.81      0.514\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      1.47G    0.03305    0.03058   0.004492        178        640: 1\n",
      "tensor([1.05939], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.825      0.733      0.801      0.508\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      1.47G    0.03301    0.03019    0.00457        148        640: 1\n",
      "tensor([0.83417], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.864        0.7      0.801      0.511\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      1.47G    0.03302    0.03014   0.004529        115        640: 1\n",
      "tensor([0.82739], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856      0.724      0.799       0.51\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      1.47G    0.03304    0.02996   0.004602        124        640: 1\n",
      "tensor([0.84154], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.854      0.719      0.802      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      1.47G    0.03284    0.02975   0.004547        163        640: 1\n",
      "tensor([0.87267], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.859      0.718      0.806      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      1.47G    0.03279    0.03014   0.004563        200        640: 1\n",
      "tensor([0.94353], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868      0.719      0.805       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      1.47G    0.03282    0.03022   0.004456        141        640: 1\n",
      "tensor([0.82189], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.722      0.802      0.516\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      1.47G    0.03289    0.03017   0.004585        146        640: 1\n",
      "tensor([0.86326], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.876      0.716      0.809       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      1.47G    0.03288    0.03025    0.00453        168        640: 1\n",
      "tensor([0.92236], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.835      0.749      0.816      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      1.47G     0.0328    0.02989   0.004443        175        640: 1\n",
      "tensor([0.92040], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.728       0.82      0.521\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      1.47G    0.03244    0.03007   0.004417        139        640: 1\n",
      "tensor([0.93322], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866      0.719      0.814      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      1.47G    0.03246    0.02922    0.00434        117        640: 1\n",
      "tensor([0.76833], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868      0.717      0.811      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      1.47G    0.03272    0.02972   0.004185        129        640: 1\n",
      "tensor([0.80499], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867      0.736      0.807      0.515\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      1.47G    0.03258    0.02993   0.004393        109        640: 1\n",
      "tensor([0.74037], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.722       0.81      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      1.47G    0.03252    0.03001   0.004282        154        640: 1\n",
      "tensor([0.94706], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865      0.719      0.811      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      1.47G    0.03216    0.02989   0.004302        119        640: 1\n",
      "tensor([0.83241], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.86      0.711      0.809      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      1.47G     0.0323    0.02924   0.004314        153        640: 1\n",
      "tensor([0.85090], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.864      0.719      0.815      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      1.47G    0.03237    0.02988   0.004292        116        640: 1\n",
      "tensor([0.78789], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.863      0.738      0.808      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      1.47G    0.03199    0.02912   0.004362        141        640: 1\n",
      "tensor([0.93503], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.722      0.819      0.528\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      1.47G    0.03216    0.02948   0.004254        175        640: 1\n",
      "tensor([1.01590], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.864      0.748       0.82      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      1.47G    0.03233    0.02968   0.004296        161        640: 1\n",
      "tensor([0.96094], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.747      0.822      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      1.47G    0.03213    0.02933   0.004143        114        640: 1\n",
      "tensor([0.82618], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.735      0.813      0.526\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      1.47G    0.03234    0.02986    0.00436        141        640: 1\n",
      "tensor([0.86715], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866      0.751       0.82      0.526\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      1.47G    0.03216    0.02984   0.004064        133        640: 1\n",
      "tensor([0.79187], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.735      0.811      0.531\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      1.47G    0.03202    0.02924   0.004135        159        640: 1\n",
      "tensor([0.94582], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.835       0.75      0.817       0.53\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      1.47G    0.03215    0.02932    0.00424        122        640: 1\n",
      "tensor([0.75337], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.876      0.733      0.811      0.531\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      1.47G    0.03212    0.02977   0.004145        137        640: 1\n",
      "tensor([0.85106], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.726       0.82      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      1.47G    0.03209    0.02914   0.004105        137        640: 1\n",
      "tensor([0.81459], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.855      0.741      0.818      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      1.47G    0.03169    0.02891   0.004131        161        640: 1\n",
      "tensor([0.98054], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.864       0.72      0.807      0.528\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      1.47G    0.03185    0.02946   0.003985        154        640: 1\n",
      "tensor([0.83240], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.829      0.761      0.816      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      1.47G    0.03179    0.02907   0.004101        181        640: 1\n",
      "tensor([1.01710], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881      0.746      0.825      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      1.47G    0.03174    0.02903     0.0041        149        640: 1\n",
      "tensor([0.83383], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.723       0.82      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      1.47G    0.03158    0.02905   0.003898        118        640: 1\n",
      "tensor([0.84145], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.741       0.82      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      1.47G    0.03153    0.02909   0.004018        178        640: 1\n",
      "tensor([0.96878], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.876      0.749      0.822      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      1.47G    0.03152    0.02913   0.003939        140        640: 1\n",
      "tensor([0.89930], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.725      0.818      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      1.47G    0.03161    0.02919   0.004001        119        640: 1\n",
      "tensor([0.71418], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.752      0.826       0.54\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      1.47G    0.03156    0.02939   0.003977        114        640: 1\n",
      "tensor([0.70035], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87      0.746      0.818      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      1.47G    0.03172    0.02909   0.004029        117        640: 1\n",
      "tensor([0.74746], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.875      0.739      0.821      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      1.47G    0.03155    0.02917   0.003874        118        640: 1\n",
      "tensor([0.76890], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.737      0.815      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      1.47G    0.03144     0.0288   0.003827        115        640: 1\n",
      "tensor([0.73976], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872       0.74      0.823      0.539\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      1.47G    0.03139    0.02864   0.003867        159        640: 1\n",
      "tensor([0.98878], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.863      0.741      0.823      0.542\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      1.47G    0.03137    0.02893   0.003942        165        640: 1\n",
      "tensor([0.90560], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.729      0.818      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      1.47G    0.03119    0.02859   0.003859        126        640: 1\n",
      "tensor([0.78203], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867      0.749      0.821      0.539\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      1.47G    0.03121    0.02873   0.003923        112        640: 1\n",
      "tensor([0.74184], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.876      0.739      0.825       0.54\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      1.47G    0.03103    0.02852   0.003756        121        640: 1\n",
      "tensor([0.75210], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.842      0.754      0.819       0.54\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      1.47G    0.03141    0.02852   0.003868        195        640: 1\n",
      "tensor([0.84179], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.734      0.824      0.542\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      1.47G    0.03135    0.02874    0.00383        101        640: 1\n",
      "tensor([0.74694], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865      0.751      0.826      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      1.47G    0.03114    0.02872    0.00391        137        640: 1\n",
      "tensor([0.78883], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866      0.749      0.824      0.545\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      1.47G    0.03107    0.02882    0.00374        115        640: 1\n",
      "tensor([0.66739], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.848      0.753      0.822      0.544\n",
      "\n",
      "100 epochs completed in 1.043 hours.\n",
      "Optimizer stripped from runs/train/not_freeze:13_17_20_23_24/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/not_freeze:13_17_20_23_24/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/not_freeze:13_17_20_23_24/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868      0.749      0.824      0.545\n",
      "                   Car       1048       4012      0.918      0.865      0.932      0.689\n",
      "                   Van       1048        431      0.906      0.812      0.888      0.654\n",
      "                 Truck       1048        166      0.946       0.88      0.921      0.667\n",
      "                  Tram       1048         56      0.886      0.821      0.903       0.62\n",
      "            Pedestrian       1048        618      0.851      0.629      0.739      0.386\n",
      "        Person_sitting       1048         20       0.76       0.65      0.675      0.382\n",
      "               Cyclist       1048        234      0.885      0.688      0.785      0.491\n",
      "                  Misc       1048        138      0.789       0.65      0.752      0.467\n",
      "Results saved to \u001b[1mruns/train/not_freeze:13_17_20_23_24\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : not_freeze:13_17_20_23_24\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/841143a6527d4587a08a8dbe7f697f44\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.890725821085826\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 308.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9322936840512378\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.6891299144101045\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9184511403542852\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.8646253431896503\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3469.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.7740474950155977\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 21.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.7850862991356066\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.49063743479120403\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.884638982624033\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.688034188034188\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 161.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.7126748397658823\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 24.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.7515848855226885\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.46704497060492284\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.7888867178675\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.6498909117056565\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 90.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7234248955484631\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 68.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.7391237566485896\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.3862015565563934\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8511221690422428\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.6290465222504057\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 389.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7005210191476801\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.6751779193047925\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.38231017331379274\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.7595573439432455\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.65\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 13.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.8523496722465391\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 6.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.902676490154986\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.6202910400702168\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.8856897621603503\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.8214285714285714\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 46.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9113552878235104\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 8.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.9213532175197614\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.6672140713368445\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9455839889857485\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.8795180722891566\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 146.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.8565104654217593\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 36.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.887703475618585\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.6540958135625888\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9061027931574583\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.8120649651972158\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 350.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.75150066614151, 2.199005603790283)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.64015228324076, 0.8261545403147075)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.38742837532735563, 0.5447356192633525)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.7622736852064396, 0.8935394923918885)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.5735000689096539, 0.7607068910277898)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.031032169237732887, 0.04157144948840141)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.003740159561857581, 0.012146929278969765)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.028519848361611366, 0.0410657562315464)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.030289139598608017, 0.03652600198984146)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.004291787277907133, 0.008754488080739975)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.04579503461718559, 0.05244307219982147)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : not_freeze:13_17_20_23_24\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/841143a6527d4587a08a8dbe7f697f44\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bbox_interval       : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg                 : models/yolov5s_kitti.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data                : data/kitti.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs              : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 19, 21, 22]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : not_freeze:13_17_20_23_24\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/not_freeze:13_17_20_23_24\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : ./runs/train/fog_02/weights/best.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.96 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m All assets have been sent, waiting for delivery confirmation\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--name not_freeze:{dont_freeze_str} \\\n",
    "--freeze {freeze_layer} \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "613d1512-b95e-466b-801b-b0258fb422a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/not_freeze:13_17_20_23_24/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 bda8da72 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.368      0.147      0.151     0.0794\n",
      "                   Car       2244       8711      0.813      0.348      0.452      0.259\n",
      "                   Van       2244        861      0.234      0.218      0.171      0.103\n",
      "                 Truck       2244        333      0.147     0.0541     0.0357     0.0222\n",
      "                  Tram       2244        138       0.14     0.0145     0.0143    0.00566\n",
      "            Pedestrian       2244       1286      0.492      0.337      0.337      0.149\n",
      "        Person_sitting       2244         89      0.414     0.0225       0.04     0.0109\n",
      "               Cyclist       2244        496      0.201     0.0766     0.0476     0.0231\n",
      "                  Misc       2244        284      0.502      0.106      0.108     0.0624\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp90\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾测试集\n",
    "model = f'runs/train/not_freeze:13_17_20_23_24/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5cc6a26-9954-42a2-8897-8b0e1d068c18",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbb65699-d7d1-47c0-b3fd-1503e8246cfc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0835bbbf-6a6b-4ba7-92de-005c62ab0760",
   "metadata": {},
   "outputs": [],
   "source": [
    "dont_freeze = [13, 17, 20, 23, 24] + [10, 14, 18, 21]\n",
    "freeze_layer = ' '.join([str(i) for i in range(25) if i not in dont_freeze]) # 冻结0 ~ 23\n",
    "dont_freeze_str = '_'.join([str(i) for i in dont_freeze])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18e43a75-cc1a-4a23-b1a5-afefc910e1d7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=not_freeze:13_17_20_23_24_10_14_18_21, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 15, 16, 19, 22], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 0f1025d8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/bfd2b88b98e54e12b3cb47ecc1d44769\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "freezing model.0.conv.weight\n",
      "freezing model.0.bn.weight\n",
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      "freezing model.1.conv.weight\n",
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      "freezing model.4.cv3.conv.weight\n",
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      "freezing model.4.m.0.cv1.conv.weight\n",
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      "freezing model.4.m.1.cv1.conv.weight\n",
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      "freezing model.4.m.1.cv1.bn.bias\n",
      "freezing model.4.m.1.cv2.conv.weight\n",
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      "freezing model.5.conv.weight\n",
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      "freezing model.6.cv1.conv.weight\n",
      "freezing model.6.cv1.bn.weight\n",
      "freezing model.6.cv1.bn.bias\n",
      "freezing model.6.cv2.conv.weight\n",
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      "freezing model.6.cv2.bn.bias\n",
      "freezing model.6.cv3.conv.weight\n",
      "freezing model.6.cv3.bn.weight\n",
      "freezing model.6.cv3.bn.bias\n",
      "freezing model.6.m.0.cv1.conv.weight\n",
      "freezing model.6.m.0.cv1.bn.weight\n",
      "freezing model.6.m.0.cv1.bn.bias\n",
      "freezing model.6.m.0.cv2.conv.weight\n",
      "freezing model.6.m.0.cv2.bn.weight\n",
      "freezing model.6.m.0.cv2.bn.bias\n",
      "freezing model.6.m.1.cv1.conv.weight\n",
      "freezing model.6.m.1.cv1.bn.weight\n",
      "freezing model.6.m.1.cv1.bn.bias\n",
      "freezing model.6.m.1.cv2.conv.weight\n",
      "freezing model.6.m.1.cv2.bn.weight\n",
      "freezing model.6.m.1.cv2.bn.bias\n",
      "freezing model.6.m.2.cv1.conv.weight\n",
      "freezing model.6.m.2.cv1.bn.weight\n",
      "freezing model.6.m.2.cv1.bn.bias\n",
      "freezing model.6.m.2.cv2.conv.weight\n",
      "freezing model.6.m.2.cv2.bn.weight\n",
      "freezing model.6.m.2.cv2.bn.bias\n",
      "freezing model.7.conv.weight\n",
      "freezing model.7.bn.weight\n",
      "freezing model.7.bn.bias\n",
      "freezing model.8.cv1.conv.weight\n",
      "freezing model.8.cv1.bn.weight\n",
      "freezing model.8.cv1.bn.bias\n",
      "freezing model.8.cv2.conv.weight\n",
      "freezing model.8.cv2.bn.weight\n",
      "freezing model.8.cv2.bn.bias\n",
      "freezing model.8.cv3.conv.weight\n",
      "freezing model.8.cv3.bn.weight\n",
      "freezing model.8.cv3.bn.bias\n",
      "freezing model.8.m.0.cv1.conv.weight\n",
      "freezing model.8.m.0.cv1.bn.weight\n",
      "freezing model.8.m.0.cv1.bn.bias\n",
      "freezing model.8.m.0.cv2.conv.weight\n",
      "freezing model.8.m.0.cv2.bn.weight\n",
      "freezing model.8.m.0.cv2.bn.bias\n",
      "freezing model.9.cv1.conv.weight\n",
      "freezing model.9.cv1.bn.weight\n",
      "freezing model.9.cv1.bn.bias\n",
      "freezing model.9.cv2.conv.weight\n",
      "freezing model.9.cv2.bn.weight\n",
      "freezing model.9.cv2.bn.bias\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/not_freeze:13_17_20_23_24_10_14_18_21/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/not_freeze:13_17_20_23_24_10_14_18_21\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      1.48G    0.04137    0.04062    0.01187        128        640: 1\n",
      "tensor([1.11896], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.781      0.581      0.645      0.392\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      1.48G    0.03919    0.03548   0.009186        133        640: 1\n",
      "tensor([1.15560], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.775      0.618      0.689       0.41\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      1.48G    0.04004    0.03538   0.008736        131        640: 1\n",
      "tensor([1.07682], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.849      0.604      0.708      0.402\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      1.48G    0.03992    0.03516    0.00817        108        640: 1\n",
      "tensor([0.88611], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.784      0.633      0.707      0.416\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      1.48G    0.03925     0.0344    0.00766        156        640: 1\n",
      "tensor([1.00223], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.827      0.643      0.722      0.417\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      1.48G    0.03834    0.03398   0.007339        123        640: 1\n",
      "tensor([0.94780], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.839      0.631      0.725      0.425\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      1.48G    0.03806     0.0335   0.007081        174        640: 1\n",
      "tensor([1.16886], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.808      0.661      0.736      0.418\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      1.48G    0.03752    0.03288   0.006727        166        640: 1\n",
      "tensor([1.18900], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.836      0.667      0.746      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      1.48G     0.0372    0.03306   0.006557        152        640: 1\n",
      "tensor([1.00069], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.832      0.661       0.75      0.439\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      1.48G    0.03735    0.03317   0.006573        136        640: 1\n",
      "tensor([0.98454], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.823      0.687      0.756      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      1.48G    0.03701    0.03294   0.006336        134        640: 1\n",
      "tensor([0.98402], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.796      0.679       0.75      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      1.48G    0.03686    0.03248   0.006263        182        640: 1\n",
      "tensor([1.02493], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.834      0.682      0.758      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      1.48G    0.03663    0.03264   0.006091        128        640: 1\n",
      "tensor([0.86198], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.84      0.673      0.755      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      1.48G    0.03622    0.03216   0.006034        112        640: 1\n",
      "tensor([0.98715], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.842      0.695      0.775      0.468\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      1.48G    0.03605    0.03232   0.005877        151        640: 1\n",
      "tensor([0.91238], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.826      0.684      0.776      0.471\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      1.48G    0.03609    0.03241    0.00578        132        640: 1\n",
      "tensor([0.96744], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.842      0.702      0.778      0.476\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      1.48G    0.03595    0.03205   0.005782        131        640: 1\n",
      "tensor([0.89648], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.659      0.773      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      1.48G    0.03616    0.03211   0.005744        159        640: 1\n",
      "tensor([1.05359], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.831      0.699       0.78      0.471\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      1.48G    0.03575     0.0318   0.005607        125        640: 1\n",
      "tensor([0.89499], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.839      0.689      0.776      0.473\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      1.48G    0.03559    0.03199   0.005716         88        640: 1\n",
      "tensor([0.76188], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.845      0.701      0.785      0.481\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      1.48G    0.03521     0.0312   0.005555        137        640: 1\n",
      "tensor([1.11271], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.82       0.69      0.773      0.482\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      1.48G    0.03539    0.03192   0.005286        166        640: 1\n",
      "tensor([1.03875], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.685      0.785      0.492\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      1.48G    0.03508    0.03158   0.005399        161        640: 1\n",
      "tensor([1.01667], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.815      0.724       0.78      0.484\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      1.48G    0.03516    0.03124   0.005248        118        640: 1\n",
      "tensor([0.87003], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866      0.683       0.79      0.487\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      1.48G    0.03467    0.03123   0.005289        151        640: 1\n",
      "tensor([1.00554], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.827      0.695      0.777      0.482\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      1.48G    0.03486    0.03154   0.005231        133        640: 1\n",
      "tensor([0.90365], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856      0.682      0.781      0.491\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      1.48G    0.03465    0.03124    0.00519        154        640: 1\n",
      "tensor([1.04655], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865      0.702      0.797      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      1.48G     0.0348    0.03136   0.005257        122        640: 1\n",
      "tensor([0.84212], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.863       0.71      0.793      0.498\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      1.48G    0.03459    0.03122   0.004888        123        640: 1\n",
      "tensor([0.78066], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.707      0.798      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      1.48G    0.03441    0.03091   0.004961        127        640: 1\n",
      "tensor([0.77490], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.835      0.718      0.797      0.497\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      1.48G    0.03435    0.03001    0.00501        127        640: 1\n",
      "tensor([0.79969], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.852      0.698      0.795      0.497\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      1.48G    0.03415     0.0306   0.004953        122        640: 1\n",
      "tensor([0.90238], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88        0.7      0.798        0.5\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      1.48G    0.03424    0.03099   0.004834        146        640: 1\n",
      "tensor([0.95555], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.869      0.696      0.798      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      1.48G    0.03398    0.03042   0.004776        202        640: 1\n",
      "tensor([1.06846], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.863      0.713      0.803      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      1.48G    0.03398    0.03035   0.004811         94        640: 1\n",
      "tensor([0.68710], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88        0.7      0.806       0.51\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      1.48G    0.03396    0.03042   0.004773        152        640: 1\n",
      "tensor([1.03848], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.697        0.8      0.505\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      1.48G    0.03393    0.03038   0.004813        123        640: 1\n",
      "tensor([0.81109], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.873      0.723      0.806      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      1.48G    0.03365    0.03042   0.004876        162        640: 1\n",
      "tensor([0.89567], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856       0.74      0.808      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      1.48G    0.03387    0.03072   0.004799        161        640: 1\n",
      "tensor([0.89122], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.713      0.809      0.515\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      1.48G    0.03363    0.03055   0.004652        122        640: 1\n",
      "tensor([0.82606], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.701      0.803      0.509\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      1.48G    0.03361    0.03021   0.004742        126        640: 1\n",
      "tensor([0.77717], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.853      0.729      0.811      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      1.48G    0.03333    0.03017   0.004586         90        640: 1\n",
      "tensor([0.73145], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.703      0.808      0.512\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      1.48G     0.0333    0.03013   0.004542        118        640: 1\n",
      "tensor([0.88005], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866      0.717       0.81      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      1.48G    0.03326    0.03019   0.004445        157        640: 1\n",
      "tensor([0.92269], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.858      0.737      0.817      0.521\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      1.48G    0.03323    0.03024   0.004358        104        640: 1\n",
      "tensor([0.68351], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.721       0.82      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      1.48G    0.03316    0.03055   0.004437        157        640: 1\n",
      "tensor([0.89625], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.732      0.817      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      1.48G    0.03313    0.02985   0.004294        108        640: 1\n",
      "tensor([0.72372], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.864       0.72      0.809      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      1.48G     0.0332    0.03006   0.004534        159        640: 1\n",
      "tensor([0.88579], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.846       0.74      0.808      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      1.48G    0.03274     0.0299   0.004344        118        640: 1\n",
      "tensor([0.87125], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867      0.719      0.809      0.511\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      1.48G     0.0329     0.0303   0.004383        176        640: 1\n",
      "tensor([1.02904], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87       0.73      0.822       0.53\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      1.48G    0.03305    0.02987   0.004467        130        640: 1\n",
      "tensor([0.80803], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.707      0.819      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      1.48G    0.03257    0.03007   0.004195        178        640: 1\n",
      "tensor([1.04938], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856      0.728      0.811      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      1.48G    0.03256    0.02968   0.004253        148        640: 1\n",
      "tensor([0.80912], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.736      0.818      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      1.48G    0.03251     0.0296   0.004163        115        640: 1\n",
      "tensor([0.80526], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867       0.73      0.814      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      1.48G     0.0326    0.02946    0.00434        124        640: 1\n",
      "tensor([0.81111], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.858      0.752      0.821      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      1.48G    0.03231    0.02917   0.004269        163        640: 1\n",
      "tensor([0.85989], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.864      0.744      0.821      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      1.48G    0.03232    0.02957    0.00424        200        640: 1\n",
      "tensor([0.93989], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.849      0.737      0.822      0.533\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      1.48G    0.03229    0.02964   0.004178        141        640: 1\n",
      "tensor([0.80471], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.844      0.748      0.822      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      1.48G    0.03242    0.02959   0.004278        146        640: 1\n",
      "tensor([0.85297], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.713      0.813      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      1.48G    0.03235     0.0297   0.004273        168        640: 1\n",
      "tensor([0.88795], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.718      0.816      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      1.48G    0.03223    0.02934   0.004102        175        640: 1\n",
      "tensor([0.91496], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.726      0.825      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      1.48G    0.03193    0.02948   0.004142        139        640: 1\n",
      "tensor([0.92301], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.739      0.824      0.539\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      1.48G    0.03193    0.02863   0.004051        117        640: 1\n",
      "tensor([0.74844], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867      0.756      0.825      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      1.48G    0.03219    0.02918   0.003899        129        640: 1\n",
      "tensor([0.79759], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.876      0.753      0.829      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      1.48G      0.032     0.0293    0.00409        109        640: 1\n",
      "tensor([0.74879], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.738      0.818      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      1.48G    0.03196    0.02941   0.003968        154        640: 1\n",
      "tensor([0.92587], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.869      0.731      0.815      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      1.48G    0.03158     0.0293   0.003965        119        640: 1\n",
      "tensor([0.82467], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.846      0.746      0.823      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      1.48G    0.03175    0.02864   0.004006        153        640: 1\n",
      "tensor([0.84371], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.722       0.83      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      1.48G    0.03176    0.02927   0.004052        116        640: 1\n",
      "tensor([0.77344], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.739      0.831      0.541\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      1.48G    0.03136     0.0285   0.004069        141        640: 1\n",
      "tensor([0.91108], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.735      0.828      0.548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      1.48G    0.03157    0.02886   0.003922        175        640: 1\n",
      "tensor([1.00300], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.748      0.829      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      1.48G     0.0317    0.02903    0.00398        161        640: 1\n",
      "tensor([0.92262], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.751      0.834      0.548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      1.48G    0.03148    0.02864   0.003881        114        640: 1\n",
      "tensor([0.82825], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.742      0.825      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      1.48G    0.03172     0.0292   0.004009        141        640: 1\n",
      "tensor([0.85054], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.746      0.835      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      1.48G    0.03153    0.02911   0.003755        133        640: 1\n",
      "tensor([0.75021], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.737       0.83      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      1.48G    0.03136    0.02863   0.003808        159        640: 1\n",
      "tensor([0.94157], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.739      0.827      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      1.48G    0.03149    0.02866   0.003927        122        640: 1\n",
      "tensor([0.72911], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.744       0.83      0.545\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      1.48G    0.03145    0.02908   0.003776        137        640: 1\n",
      "tensor([0.82082], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881      0.742      0.838      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      1.48G    0.03143    0.02843   0.003786        137        640: 1\n",
      "tensor([0.79774], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.742      0.833       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      1.48G    0.03105    0.02821   0.003823        161        640: 1\n",
      "tensor([0.95601], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.738      0.832      0.549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      1.48G    0.03114    0.02871   0.003652        154        640: 1\n",
      "tensor([0.80950], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87      0.761      0.834      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      1.48G     0.0311     0.0284   0.003826        181        640: 1\n",
      "tensor([0.98569], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888       0.75      0.837      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      1.48G    0.03107    0.02833    0.00377        149        640: 1\n",
      "tensor([0.81880], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.748      0.835      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      1.48G    0.03086    0.02837     0.0036        118        640: 1\n",
      "tensor([0.84770], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.749       0.83      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      1.48G    0.03081     0.0284   0.003676        178        640: 1\n",
      "tensor([0.94665], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.873      0.756       0.83      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      1.48G    0.03078    0.02836   0.003612        140        640: 1\n",
      "tensor([0.87975], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868      0.752      0.835      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      1.48G    0.03091    0.02846   0.003676        119        640: 1\n",
      "tensor([0.69689], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865      0.766      0.837      0.559\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      1.48G    0.03086    0.02865   0.003642        114        640: 1\n",
      "tensor([0.68086], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.763      0.834      0.557\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      1.48G    0.03095    0.02829   0.003657        117        640: 1\n",
      "tensor([0.73339], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.753      0.839      0.558\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      1.48G    0.03083    0.02847   0.003577        118        640: 1\n",
      "tensor([0.75499], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865       0.76      0.835      0.559\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      1.48G    0.03068    0.02807   0.003508        115        640: 1\n",
      "tensor([0.72756], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.752      0.837      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      1.48G    0.03062    0.02786   0.003532        159        640: 1\n",
      "tensor([0.97130], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.749       0.84      0.564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      1.48G    0.03062    0.02822   0.003616        165        640: 1\n",
      "tensor([0.89406], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.758      0.837      0.562\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      1.48G    0.03046    0.02784   0.003509        126        640: 1\n",
      "tensor([0.75150], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.757      0.837       0.56\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      1.48G    0.03043    0.02795   0.003581        112        640: 1\n",
      "tensor([0.70960], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.752      0.836      0.562\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      1.48G    0.03024    0.02776   0.003416        121        640: 1\n",
      "tensor([0.72075], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.749      0.836      0.564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      1.48G    0.03061     0.0278   0.003566        195        640: 1\n",
      "tensor([0.82742], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.749      0.838      0.564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      1.48G    0.03054    0.02795   0.003459        101        640: 1\n",
      "tensor([0.74052], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.765      0.838      0.566\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      1.48G    0.03035    0.02795   0.003571        137        640: 1\n",
      "tensor([0.74307], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874      0.755      0.836      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      1.48G    0.03031    0.02805   0.003451        115        640: 1\n",
      "tensor([0.64637], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.873      0.751      0.836      0.565\n",
      "\n",
      "100 epochs completed in 1.018 hours.\n",
      "Optimizer stripped from runs/train/not_freeze:13_17_20_23_24_10_14_18_21/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/not_freeze:13_17_20_23_24_10_14_18_21/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/not_freeze:13_17_20_23_24_10_14_18_21/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.765      0.838      0.566\n",
      "                   Car       1048       4012      0.912      0.873      0.937      0.701\n",
      "                   Van       1048        431      0.892      0.828        0.9      0.673\n",
      "                 Truck       1048        166      0.934      0.856      0.916      0.667\n",
      "                  Tram       1048         56      0.848      0.895      0.954      0.674\n",
      "            Pedestrian       1048        618      0.841      0.633      0.726      0.396\n",
      "        Person_sitting       1048         20      0.916       0.65      0.699      0.414\n",
      "               Cyclist       1048        234      0.845      0.701      0.798      0.499\n",
      "                  Misc       1048        138      0.789      0.681      0.771      0.503\n",
      "Results saved to \u001b[1mruns/train/not_freeze:13_17_20_23_24_10_14_18_21\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : not_freeze:13_17_20_23_24_10_14_18_21\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/bfd2b88b98e54e12b3cb47ecc1d44769\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.8919854720359933\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 337.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.937214893691757\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.7005910536505285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9121956253293313\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.8726514400741718\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3501.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.7663920313923809\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 30.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.7981546606166583\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.4988645555361454\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.8454506094031523\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.7008547008547008\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 164.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.7310620532212997\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 25.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.7709129768412051\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.5031146374787466\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.7888545744624176\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.6811594202898551\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 94.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7222301387048203\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 74.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.7261263202709846\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.39636325813885515\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8412992061397697\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.6326860841423948\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 391.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7605002299655648\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.6991352299402118\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.4141060716167921\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.9162657278724425\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.65\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 13.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.8705620812992256\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 9.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.9537743756243756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.6741214324317798\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.8477162180950145\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.8946734366197454\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 50.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.8933816508755252\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.9161670776068561\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.6665654656416641\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9342475946879112\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.8559410012422061\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 142.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.8591003078981538\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 43.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.899725467113901\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.6734980909704545\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.8924793071722896\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.8281280647869975\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 357.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.7130246162414551, 2.199005603790283)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.644847239177025, 0.8396654991561314)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.3921009107056614, 0.5658366900266092)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.7745454563910859, 0.9054963262931649)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.5805642632738396, 0.7659849158882022)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.0302447322756052, 0.04137134924530983)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.003416025312617421, 0.011873123236000538)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.027757856994867325, 0.04061666131019592)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.02960004098713398, 0.03630545362830162)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.004163903649896383, 0.008431256748735905)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.0451989620923996, 0.05214032158255577)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : not_freeze:13_17_20_23_24_10_14_18_21\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/bfd2b88b98e54e12b3cb47ecc1d44769\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bbox_interval       : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg                 : models/yolov5s_kitti.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data                : data/kitti.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs              : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 15, 16, 19, 22]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : not_freeze:13_17_20_23_24_10_14_18_21\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/not_freeze:13_17_20_23_24_10_14_18_21\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : ./runs/train/fog_02/weights/best.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.92 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--name not_freeze:{dont_freeze_str} \\\n",
    "--freeze {freeze_layer} \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f702c0c1-c0da-47a7-8756-f82a4322fac6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/not_freeze:13_17_20_23_24_10_14_18_21/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 bda8da72 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.306      0.157      0.152     0.0817\n",
      "                   Car       2244       8711      0.762       0.37      0.443      0.259\n",
      "                   Van       2244        861      0.211      0.251      0.173      0.101\n",
      "                 Truck       2244        333      0.158     0.0691     0.0506     0.0328\n",
      "                  Tram       2244        138     0.0478    0.00725    0.00784    0.00278\n",
      "            Pedestrian       2244       1286      0.469       0.32      0.316      0.145\n",
      "        Person_sitting       2244         89      0.197     0.0196     0.0431     0.0108\n",
      "               Cyclist       2244        496      0.198     0.0927     0.0637     0.0329\n",
      "                  Misc       2244        284      0.403      0.127      0.121     0.0695\n",
      "Speed: 0.1ms pre-process, 0.8ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp91\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾测试集\n",
    "model = f'runs/train/not_freeze:13_17_20_23_24_10_14_18_21/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8d7c9fb-3d5e-4474-8a4c-0844480110d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65e42d6c-fe23-4c91-81de-cda1469924b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e5f338f-d497-49e3-9d0e-429e6d8983bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "464910b9-a014-42bb-b213-d88b28bafa71",
   "metadata": {},
   "outputs": [],
   "source": [
    "dont_freeze = [13, 17, 20, 23, 24] + [10, 14, 18, 21] + [7, 8, 9]\n",
    "freeze_layer = ' '.join([str(i) for i in range(25) if i not in dont_freeze]) # 冻结0 ~ 23\n",
    "dont_freeze_str = '_'.join([str(i) for i in dont_freeze])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "02d3cf49-c905-443d-98de-d7a7e577d5f1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=not_freeze:13_17_20_23_24_10_14_18_21_7_8_9, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0, 1, 2, 3, 4, 5, 6, 11, 12, 15, 16, 19, 22], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2882 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 0f1025d8 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/cee317ac2550437097b18d90ea1fa535\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/349 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "freezing model.0.conv.weight\n",
      "freezing model.0.bn.weight\n",
      "freezing model.0.bn.bias\n",
      "freezing model.1.conv.weight\n",
      "freezing model.1.bn.weight\n",
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      "freezing model.2.cv1.conv.weight\n",
      "freezing model.2.cv1.bn.weight\n",
      "freezing model.2.cv1.bn.bias\n",
      "freezing model.2.cv2.conv.weight\n",
      "freezing model.2.cv2.bn.weight\n",
      "freezing model.2.cv2.bn.bias\n",
      "freezing model.2.cv3.conv.weight\n",
      "freezing model.2.cv3.bn.weight\n",
      "freezing model.2.cv3.bn.bias\n",
      "freezing model.2.m.0.cv1.conv.weight\n",
      "freezing model.2.m.0.cv1.bn.weight\n",
      "freezing model.2.m.0.cv1.bn.bias\n",
      "freezing model.2.m.0.cv2.conv.weight\n",
      "freezing model.2.m.0.cv2.bn.weight\n",
      "freezing model.2.m.0.cv2.bn.bias\n",
      "freezing model.3.conv.weight\n",
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      "freezing model.4.cv1.conv.weight\n",
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      "freezing model.4.cv1.bn.bias\n",
      "freezing model.4.cv2.conv.weight\n",
      "freezing model.4.cv2.bn.weight\n",
      "freezing model.4.cv2.bn.bias\n",
      "freezing model.4.cv3.conv.weight\n",
      "freezing model.4.cv3.bn.weight\n",
      "freezing model.4.cv3.bn.bias\n",
      "freezing model.4.m.0.cv1.conv.weight\n",
      "freezing model.4.m.0.cv1.bn.weight\n",
      "freezing model.4.m.0.cv1.bn.bias\n",
      "freezing model.4.m.0.cv2.conv.weight\n",
      "freezing model.4.m.0.cv2.bn.weight\n",
      "freezing model.4.m.0.cv2.bn.bias\n",
      "freezing model.4.m.1.cv1.conv.weight\n",
      "freezing model.4.m.1.cv1.bn.weight\n",
      "freezing model.4.m.1.cv1.bn.bias\n",
      "freezing model.4.m.1.cv2.conv.weight\n",
      "freezing model.4.m.1.cv2.bn.weight\n",
      "freezing model.4.m.1.cv2.bn.bias\n",
      "freezing model.5.conv.weight\n",
      "freezing model.5.bn.weight\n",
      "freezing model.5.bn.bias\n",
      "freezing model.6.cv1.conv.weight\n",
      "freezing model.6.cv1.bn.weight\n",
      "freezing model.6.cv1.bn.bias\n",
      "freezing model.6.cv2.conv.weight\n",
      "freezing model.6.cv2.bn.weight\n",
      "freezing model.6.cv2.bn.bias\n",
      "freezing model.6.cv3.conv.weight\n",
      "freezing model.6.cv3.bn.weight\n",
      "freezing model.6.cv3.bn.bias\n",
      "freezing model.6.m.0.cv1.conv.weight\n",
      "freezing model.6.m.0.cv1.bn.weight\n",
      "freezing model.6.m.0.cv1.bn.bias\n",
      "freezing model.6.m.0.cv2.conv.weight\n",
      "freezing model.6.m.0.cv2.bn.weight\n",
      "freezing model.6.m.0.cv2.bn.bias\n",
      "freezing model.6.m.1.cv1.conv.weight\n",
      "freezing model.6.m.1.cv1.bn.weight\n",
      "freezing model.6.m.1.cv1.bn.bias\n",
      "freezing model.6.m.1.cv2.conv.weight\n",
      "freezing model.6.m.1.cv2.bn.weight\n",
      "freezing model.6.m.1.cv2.bn.bias\n",
      "freezing model.6.m.2.cv1.conv.weight\n",
      "freezing model.6.m.2.cv1.bn.weight\n",
      "freezing model.6.m.2.cv1.bn.bias\n",
      "freezing model.6.m.2.cv2.conv.weight\n",
      "freezing model.6.m.2.cv2.bn.weight\n",
      "freezing model.6.m.2.cv2.bn.bias\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train.cache... 4189 image\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val.cache... 1048 images, 0\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/not_freeze:13_17_20_23_24_10_14_18_21_7_8_9/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/not_freeze:13_17_20_23_24_10_14_18_21_7_8_9\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      1.68G     0.0405     0.0393    0.01057        128        640: 1\n",
      "tensor([1.06760], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.815       0.59      0.683      0.414\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      1.68G    0.03815    0.03408   0.007529        133        640: 1\n",
      "tensor([1.09827], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865      0.604      0.716      0.409\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      1.68G    0.03941    0.03425   0.007186        131        640: 1\n",
      "tensor([1.00572], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.778      0.649      0.718      0.405\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      1.68G     0.0396    0.03452     0.0071        108        640: 1\n",
      "tensor([0.87133], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.775      0.643      0.724      0.423\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      1.68G    0.03856    0.03368   0.006666        156        640: 1\n",
      "tensor([0.98928], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.803      0.667       0.74      0.424\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      1.68G    0.03796    0.03313    0.00623        123        640: 1\n",
      "tensor([0.92871], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.81      0.654      0.748      0.433\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      1.68G    0.03707     0.0325   0.005788        174        640: 1\n",
      "tensor([1.07956], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.802      0.674      0.762      0.438\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      1.68G    0.03704    0.03194   0.005668        166        640: 1\n",
      "tensor([1.15599], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.826      0.689      0.777      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      1.68G    0.03647    0.03204   0.005381        152        640: 1\n",
      "tensor([0.95110], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.829      0.695       0.77      0.454\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      1.68G     0.0365    0.03206    0.00542        136        640: 1\n",
      "tensor([0.96837], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.821      0.689      0.785      0.472\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      1.68G     0.0361    0.03172    0.00518        134        640: 1\n",
      "tensor([0.92266], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.828      0.706       0.78      0.484\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      1.68G    0.03576    0.03127   0.005093        182        640: 1\n",
      "tensor([1.02773], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.838       0.68      0.781      0.462\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      1.68G    0.03565    0.03144   0.004928        128        640: 1\n",
      "tensor([0.84316], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.687      0.787      0.488\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      1.68G    0.03531    0.03093    0.00472        112        640: 1\n",
      "tensor([0.97481], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.873      0.707      0.809        0.5\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      1.68G    0.03503    0.03099    0.00471        151        640: 1\n",
      "tensor([0.90794], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.857      0.718      0.808      0.497\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      1.68G    0.03515    0.03105   0.004641        132        640: 1\n",
      "tensor([0.92494], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.729      0.816      0.499\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      1.68G    0.03477    0.03066   0.004598        131        640: 1\n",
      "tensor([0.88255], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.864      0.719      0.803      0.489\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      1.68G      0.035    0.03062   0.004446        159        640: 1\n",
      "tensor([0.98171], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.724      0.821      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      1.68G    0.03448    0.03033   0.004416        125        640: 1\n",
      "tensor([0.85503], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.846      0.724      0.811      0.501\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      1.68G    0.03448    0.03059   0.004471         88        640: 1\n",
      "tensor([0.72097], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.697      0.802      0.504\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      1.68G    0.03419    0.02979   0.004414        137        640: 1\n",
      "tensor([0.99233], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.837      0.749      0.818      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      1.68G    0.03404    0.03041   0.004197        166        640: 1\n",
      "tensor([0.99219], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.738       0.82      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      1.68G    0.03389    0.03005   0.004157        161        640: 1\n",
      "tensor([0.97955], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.853      0.744      0.827      0.515\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      1.68G    0.03381    0.02965   0.004152        118        640: 1\n",
      "tensor([0.81569], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.729      0.823      0.512\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      1.68G    0.03338    0.02966   0.004134        151        640: 1\n",
      "tensor([0.92349], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867      0.732      0.825      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      1.68G     0.0335    0.02988   0.003968        133        640: 1\n",
      "tensor([0.84504], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.738      0.826      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      1.68G    0.03329    0.02946   0.003945        154        640: 1\n",
      "tensor([0.99285], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.827      0.749      0.818      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      1.68G    0.03324    0.02965   0.004069        122        640: 1\n",
      "tensor([0.80910], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.722      0.821      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      1.68G    0.03303    0.02945   0.003776        123        640: 1\n",
      "tensor([0.74671], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.743      0.832      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      1.68G    0.03306    0.02917   0.003819        127        640: 1\n",
      "tensor([0.76967], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.731      0.827      0.521\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      1.68G     0.0327    0.02829   0.003889        127        640: 1\n",
      "tensor([0.74653], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896      0.728      0.824      0.523\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      1.68G    0.03265    0.02884   0.003824        122        640: 1\n",
      "tensor([0.82327], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.92      0.709      0.825      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      1.68G    0.03278    0.02923   0.003816        146        640: 1\n",
      "tensor([0.91748], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.86      0.757       0.83      0.526\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      1.68G     0.0325    0.02853   0.003717        202        640: 1\n",
      "tensor([0.99718], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87      0.734      0.825      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      1.68G    0.03229    0.02849    0.00364         94        640: 1\n",
      "tensor([0.69119], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.831      0.772      0.831      0.531\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      1.68G    0.03228    0.02846   0.003704        152        640: 1\n",
      "tensor([0.95720], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.742      0.832      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      1.68G    0.03213    0.02846   0.003719        123        640: 1\n",
      "tensor([0.74377], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.859      0.743      0.827      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      1.68G    0.03205     0.0286   0.003724        162        640: 1\n",
      "tensor([0.83609], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.738      0.826      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      1.68G    0.03221    0.02877   0.003685        161        640: 1\n",
      "tensor([0.84499], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.877      0.751      0.836      0.542\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      1.68G    0.03188    0.02846   0.003536        122        640: 1\n",
      "tensor([0.75174], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885       0.75       0.84      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      1.68G    0.03181    0.02819   0.003614        126        640: 1\n",
      "tensor([0.74348], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.767      0.842      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      1.68G    0.03169    0.02819   0.003619         90        640: 1\n",
      "tensor([0.69326], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.748      0.842      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      1.68G    0.03137    0.02809   0.003379        118        640: 1\n",
      "tensor([0.79898], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.843      0.766      0.832      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      1.68G    0.03127    0.02836   0.003226        157        640: 1\n",
      "tensor([0.81544], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.751      0.836      0.545\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      1.68G    0.03111    0.02769   0.003211        108        640: 1\n",
      "tensor([0.65397], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.757      0.846      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      1.68G    0.03121    0.02775    0.00336        159        640: 1\n",
      "tensor([0.82695], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892       0.73      0.827      0.544\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      1.68G    0.03086     0.0277   0.003238        118        640: 1\n",
      "tensor([0.75844], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.762      0.838      0.549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      1.68G    0.03104    0.02805   0.003239        176        640: 1\n",
      "tensor([0.94832], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.742      0.844       0.56\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      1.68G    0.03096    0.02761   0.003279        130        640: 1\n",
      "tensor([0.73497], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.751      0.842      0.559\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      1.68G    0.03068    0.02783   0.003178        178        640: 1\n",
      "tensor([0.93942], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.753      0.841      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      1.68G    0.03047    0.02737   0.003202        148        640: 1\n",
      "tensor([0.76513], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.858      0.778      0.849      0.564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      1.68G    0.03049    0.02729   0.003089        115        640: 1\n",
      "tensor([0.74797], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.765       0.85      0.566\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      1.68G    0.03061    0.02718   0.003161        124        640: 1\n",
      "tensor([0.73052], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.896       0.77      0.847       0.57\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      1.68G    0.03031    0.02693   0.003143        184        640:  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      90/99      1.68G    0.02794    0.02511   0.002462        115        640: 1\n",
      "tensor([0.66193], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.909      0.769       0.86      0.595\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      1.68G    0.02783    0.02516   0.002541        165        640: 1\n",
      "tensor([0.78948], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.783      0.864      0.601\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      1.68G    0.02767     0.0248   0.002486        126        640: 1\n",
      "tensor([0.68568], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.91      0.774      0.862      0.602\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      1.68G    0.02761    0.02491   0.002538        112        640: 1\n",
      "tensor([0.64121], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.785      0.862      0.601\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      1.68G    0.02741    0.02464   0.002349        121        640: 1\n",
      "tensor([0.65028], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.907      0.785      0.861        0.6\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      1.68G    0.02767    0.02465   0.002507        195        640: 1\n",
      "tensor([0.73447], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.898      0.789      0.858      0.599\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      1.68G    0.02771    0.02485   0.002439        101        640: 1\n",
      "tensor([0.66754], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.783      0.861      0.602\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      1.68G     0.0275    0.02476   0.002509        137        640: 1\n",
      "tensor([0.64578], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.785      0.859      0.599\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      1.68G    0.02743     0.0249   0.002403        115        640: 1\n",
      "tensor([0.57182], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.783      0.857      0.602\n",
      "\n",
      "100 epochs completed in 1.027 hours.\n",
      "Optimizer stripped from runs/train/not_freeze:13_17_20_23_24_10_14_18_21_7_8_9/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/not_freeze:13_17_20_23_24_10_14_18_21_7_8_9/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/not_freeze:13_17_20_23_24_10_14_18_21_7_8_9/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.899      0.783      0.861      0.602\n",
      "                   Car       1048       4012      0.939      0.882      0.949      0.734\n",
      "                   Van       1048        431      0.932      0.879       0.94      0.731\n",
      "                 Truck       1048        166      0.937       0.88      0.939      0.718\n",
      "                  Tram       1048         56      0.885      0.946      0.951      0.676\n",
      "            Pedestrian       1048        618      0.888       0.65      0.762      0.421\n",
      "        Person_sitting       1048         20      0.858      0.606      0.703      0.452\n",
      "               Cyclist       1048        234      0.878      0.726      0.833      0.535\n",
      "                  Misc       1048        138       0.87      0.696       0.81      0.552\n",
      "Results saved to \u001b[1mruns/train/not_freeze:13_17_20_23_24_10_14_18_21_7_8_9\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : not_freeze:13_17_20_23_24_10_14_18_21_7_8_9\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/cee317ac2550437097b18d90ea1fa535\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_f1                         : 0.9097600746070861\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_false_positives            : 228.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5                     : 0.9493411133952883\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_mAP@.5:.95                 : 0.7342466655459077\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_precision                  : 0.9394895329422401\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_recall                     : 0.8818544366899302\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_support                    : 4012\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Car_true_positives             : 3538.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_f1                     : 0.7949293038973498\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_false_positives        : 24.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5                 : 0.8332985368765551\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_mAP@.5:.95             : 0.5350481651151279\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_precision              : 0.8775960491614035\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_recall                 : 0.7264957264957265\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_support                : 234\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Cyclist_true_positives         : 170.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_f1                        : 0.7732962357419082\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_false_positives           : 14.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5                    : 0.8104357488216943\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_mAP@.5:.95                : 0.5515370119228687\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_precision                 : 0.8704500749326062\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_recall                    : 0.6956521739130435\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_support                   : 138\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Misc_true_positives            : 96.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_f1                  : 0.7509636150960258\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_false_positives     : 51.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5              : 0.7622147835185603\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_mAP@.5:.95          : 0.42053276059753736\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_precision           : 0.8881535425910194\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_recall              : 0.6504854368932039\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_support             : 618\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Pedestrian_true_positives      : 402.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_f1              : 0.7104822783904423\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_false_positives : 2.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5          : 0.7031855649376758\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_mAP@.5:.95      : 0.4522768380528581\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_precision       : 0.8583041739504325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_recall          : 0.6060969132397703\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_support         : 20\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Person_sitting_true_positives  : 12.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_f1                        : 0.9145023467308541\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_false_positives           : 7.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5                    : 0.9513482335584635\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_mAP@.5:.95                : 0.6762566304112052\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_precision                 : 0.884659791122275\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_recall                    : 0.9464285714285714\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_support                   : 56\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Tram_true_positives            : 53.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_f1                       : 0.9075790852037969\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_false_positives          : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5                   : 0.938939173240702\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_mAP@.5:.95               : 0.7175304218705457\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_precision                : 0.9374896815757031\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_recall                   : 0.8795180722891566\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_support                  : 166\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Truck_true_positives           : 146.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_f1                         : 0.9050675799657784\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_false_positives            : 28.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5                     : 0.9397683070416164\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_mAP@.5:.95                 : 0.7309319442568628\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_precision                  : 0.9323343675760132\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_recall                     : 0.8793503480278422\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_support                    : 431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Van_true_positives             : 379.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [2610]                    : (0.6307691931724548, 2.199005603790283)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [200]          : (0.6833732558462151, 0.8649934084088486)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [200]     : (0.4048009357239939, 0.6022302621060838)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [200]        : (0.7752373012815146, 0.920771378984164)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [200]           : (0.5898314283806251, 0.7913692141227562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [200]           : (0.027413180097937584, 0.04050328582525253)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [200]           : (0.002348798094317317, 0.010571119375526905)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [200]           : (0.02463894709944725, 0.0392986424267292)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [200]             : (0.027755748480558395, 0.03615238144993782)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [200]             : (0.0034870030358433723, 0.007199743762612343)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [200]             : (0.04313260689377785, 0.05090991035103798)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [200]                    : (0.0002980000000000002, 0.07011450381679389)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [200]                    : (0.0002980000000000002, 0.009789529262086514)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : not_freeze:13_17_20_23_24_10_14_18_21_7_8_9\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/cee317ac2550437097b18d90ea1fa535\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bbox_interval       : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg                 : models/yolov5s_kitti.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.05000000000000001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data                : data/kitti.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs              : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0, 1, 2, 3, 4, 5, 6, 11, 12, 15, 16, 19, 22]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : not_freeze:13_17_20_23_24_10_14_18_21_7_8_9\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/not_freeze:13_17_20_23_24_10_14_18_21_7_8_9\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : ./runs/train/fog_02/weights/best.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (1.90 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m All assets have been sent, waiting for delivery confirmation\n"
     ]
    }
   ],
   "source": [
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--name not_freeze:{dont_freeze_str} \\\n",
    "--freeze {freeze_layer} \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a4ffd6e1-35d3-424e-916a-e071ee4d0174",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/not_freeze:13_17_20_23_24_10_14_18_21_7_8_9/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 bda8da72 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA vGPU-32GB, 32260MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.446      0.234      0.246      0.133\n",
      "                   Car       2244       8711      0.705      0.495      0.583      0.345\n",
      "                   Van       2244        861       0.24      0.273      0.218      0.125\n",
      "                 Truck       2244        333      0.764      0.183      0.314      0.217\n",
      "                  Tram       2244        138      0.273     0.0435     0.0621     0.0301\n",
      "            Pedestrian       2244       1286      0.572      0.335      0.354      0.145\n",
      "        Person_sitting       2244         89      0.247      0.247      0.147     0.0504\n",
      "               Cyclist       2244        496      0.409      0.129      0.121     0.0579\n",
      "                  Misc       2244        284      0.359       0.17      0.168     0.0941\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp93\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾测试集\n",
    "model = f'runs/train/not_freeze:13_17_20_23_24_10_14_18_21_7_8_9/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc9424ff-8f13-4b15-bde5-a9ef3f44fb86",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21000e0a-4bb9-445e-9ed3-b78f3e6ef704",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ad81754-e27e-49a8-b1f9-8c903f80c14a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dont_freeze = [13, 17, 20, 23, 24] + [10, 14, 18, 21] + [7, 8, 9]\n",
    "freeze_layer = ' '.join([str(i) for i in range(25) if i not in dont_freeze]) # 冻结0 ~ 23\n",
    "dont_freeze_str = '_'.join([str(i) for i in dont_freeze])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dd80947-12a8-4222-a2be-cee55d36eaaa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "692692b2-6688-4fe7-a285-676374b14b56",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93273615-963c-4f48-beac-00bcc0a17add",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bbfb29d-7576-4ddf-9f42-73accdf05b1d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c8454a0-82f4-4f40-acc1-2958e5aaa9ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86b08d8d-6491-4549-b503-8d572f77140c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e644557-653d-4e0f-a57c-539f520b884e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Val set updated successfully!\n"
     ]
    }
   ],
   "source": [
    "from fog_test.for_different_strength import mix_dataset\n",
    "origin_ratio = {\n",
    "    '1.0':1,\n",
    "}\n",
    "# 先初始化数据集训练一个没有家务数据的\n",
    "mix_dataset(fogged_folder = '../datasets/fogged/', \n",
    "            ratio = origin_ratio,\n",
    "            train_folder = '../datasets/kitti/images/origin_train', \n",
    "            out_folder = '../datasets/kitti/images/train'\n",
    "               )\n",
    "\n",
    "val_fogged_strength = 1.0\n",
    "# 替换验证集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/val/* &&\\\n",
    "cp /root/autodl-tmp/datasets/fogged/val_fogged_strength{val_fogged_strength}/* ../datasets/kitti/images/val/ && \\\n",
    "echo 'Val set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "042dd964-e42e-4e5a-ad16-6f3890cc5004",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1 2 3 4 5 6 7 8 9 0_1_2_3_4_5_6_7_8_9\n"
     ]
    }
   ],
   "source": [
    "#dont_freeze = [13, 17, 20, 23, 24] + [10, 14, 18, 21]\n",
    "freeze_layer = ' '.join([str(i) for i in range(10)]) # 冻结0 ~ 23\n",
    "#dont_freeze_str = '_'.join([str(i) for i in dont_freeze])\n",
    "freeze_layer_str = freeze_layer.replace(' ', '_')\n",
    "print(freeze_layer, freeze_layer_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47690bbf-0593-4e8b-a257-82f2460f22d8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_SI: \u001b[0mweights=./runs/train/fog_02/weights/best.pt, cfg=models/yolov5s_kitti.yaml, data=data/kitti.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=not_freeze:0_1_2_3_4_5_6_7_8_9, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "fatal: unable to access 'https://github.com/ultralytics/yolov5/': HTTP/2 stream 1 was not closed cleanly before end of the underlying stream\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/922fa7f66b9647a58582b5a2c4d364e6\u001b[0m\n",
      "\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_kitti summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "Transferred 348/355 items from runs/train/fog_02/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "freezing model.0.conv.weight\n",
      "freezing model.0.bn.weight\n",
      "freezing model.0.bn.bias\n",
      "freezing model.1.conv.weight\n",
      "freezing model.1.bn.weight\n",
      "freezing model.1.bn.bias\n",
      "freezing model.2.cv1.conv.weight\n",
      "freezing model.2.cv1.bn.weight\n",
      "freezing model.2.cv1.bn.bias\n",
      "freezing model.2.cv2.conv.weight\n",
      "freezing model.2.cv2.bn.weight\n",
      "freezing model.2.cv2.bn.bias\n",
      "freezing model.2.cv3.conv.weight\n",
      "freezing model.2.cv3.bn.weight\n",
      "freezing model.2.cv3.bn.bias\n",
      "freezing model.2.m.0.cv1.conv.weight\n",
      "freezing model.2.m.0.cv1.bn.weight\n",
      "freezing model.2.m.0.cv1.bn.bias\n",
      "freezing model.2.m.0.cv2.conv.weight\n",
      "freezing model.2.m.0.cv2.bn.weight\n",
      "freezing model.2.m.0.cv2.bn.bias\n",
      "freezing model.3.conv.weight\n",
      "freezing model.3.bn.weight\n",
      "freezing model.3.bn.bias\n",
      "freezing model.4.cv1.conv.weight\n",
      "freezing model.4.cv1.bn.weight\n",
      "freezing model.4.cv1.bn.bias\n",
      "freezing model.4.cv2.conv.weight\n",
      "freezing model.4.cv2.bn.weight\n",
      "freezing model.4.cv2.bn.bias\n",
      "freezing model.4.cv3.conv.weight\n",
      "freezing model.4.cv3.bn.weight\n",
      "freezing model.4.cv3.bn.bias\n",
      "freezing model.4.m.0.cv1.conv.weight\n",
      "freezing model.4.m.0.cv1.bn.weight\n",
      "freezing model.4.m.0.cv1.bn.bias\n",
      "freezing model.4.m.0.cv2.conv.weight\n",
      "freezing model.4.m.0.cv2.bn.weight\n",
      "freezing model.4.m.0.cv2.bn.bias\n",
      "freezing model.4.m.1.cv1.conv.weight\n",
      "freezing model.4.m.1.cv1.bn.weight\n",
      "freezing model.4.m.1.cv1.bn.bias\n",
      "freezing model.4.m.1.cv2.conv.weight\n",
      "freezing model.4.m.1.cv2.bn.weight\n",
      "freezing model.4.m.1.cv2.bn.bias\n",
      "freezing model.5.conv.weight\n",
      "freezing model.5.bn.weight\n",
      "freezing model.5.bn.bias\n",
      "freezing model.6.cv1.conv.weight\n",
      "freezing model.6.cv1.bn.weight\n",
      "freezing model.6.cv1.bn.bias\n",
      "freezing model.6.cv2.conv.weight\n",
      "freezing model.6.cv2.bn.weight\n",
      "freezing model.6.cv2.bn.bias\n",
      "freezing model.6.cv3.conv.weight\n",
      "freezing model.6.cv3.bn.weight\n",
      "freezing model.6.cv3.bn.bias\n",
      "freezing model.6.m.0.cv1.conv.weight\n",
      "freezing model.6.m.0.cv1.bn.weight\n",
      "freezing model.6.m.0.cv1.bn.bias\n",
      "freezing model.6.m.0.cv2.conv.weight\n",
      "freezing model.6.m.0.cv2.bn.weight\n",
      "freezing model.6.m.0.cv2.bn.bias\n",
      "freezing model.6.m.1.cv1.conv.weight\n",
      "freezing model.6.m.1.cv1.bn.weight\n",
      "freezing model.6.m.1.cv1.bn.bias\n",
      "freezing model.6.m.1.cv2.conv.weight\n",
      "freezing model.6.m.1.cv2.bn.weight\n",
      "freezing model.6.m.1.cv2.bn.bias\n",
      "freezing model.6.m.2.cv1.conv.weight\n",
      "freezing model.6.m.2.cv1.bn.weight\n",
      "freezing model.6.m.2.cv1.bn.bias\n",
      "freezing model.6.m.2.cv2.conv.weight\n",
      "freezing model.6.m.2.cv2.bn.weight\n",
      "freezing model.6.m.2.cv2.bn.bias\n",
      "freezing model.7.conv.weight\n",
      "freezing model.7.bn.weight\n",
      "freezing model.7.bn.bias\n",
      "freezing model.8.cv1.conv.weight\n",
      "freezing model.8.cv1.bn.weight\n",
      "freezing model.8.cv1.bn.bias\n",
      "freezing model.8.cv2.conv.weight\n",
      "freezing model.8.cv2.bn.weight\n",
      "freezing model.8.cv2.bn.bias\n",
      "freezing model.8.cv3.conv.weight\n",
      "freezing model.8.cv3.bn.weight\n",
      "freezing model.8.cv3.bn.bias\n",
      "freezing model.8.m.0.cv1.conv.weight\n",
      "freezing model.8.m.0.cv1.bn.weight\n",
      "freezing model.8.m.0.cv1.bn.bias\n",
      "freezing model.8.m.0.cv2.conv.weight\n",
      "freezing model.8.m.0.cv2.bn.weight\n",
      "freezing model.8.m.0.cv2.bn.bias\n",
      "freezing model.9.cv1.conv.weight\n",
      "freezing model.9.cv1.bn.weight\n",
      "freezing model.9.cv1.bn.bias\n",
      "freezing model.9.cv2.conv.weight\n",
      "freezing model.9.cv2.bn.weight\n",
      "freezing model.9.cv2.bn.bias\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/train... 4189 images, 0 b\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/train.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/val... 1048 images, 0 backg\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/val.cache\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.81 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/not_freeze:0_1_2_3_4_5_6_7_8_9/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/not_freeze:0_1_2_3_4_5_6_7_8_9\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/99      1.48G    0.04137    0.04062    0.01187        128        640: 1\n",
      "tensor([1.11896], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.781      0.581      0.645      0.392\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/99      1.48G    0.03919    0.03548   0.009186        133        640: 1\n",
      "tensor([1.15560], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.775      0.618      0.689       0.41\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/99      1.48G    0.04004    0.03538   0.008736        131        640: 1\n",
      "tensor([1.07682], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.849      0.604      0.708      0.402\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/99      1.48G    0.03992    0.03516    0.00817        108        640: 1\n",
      "tensor([0.88611], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.784      0.633      0.707      0.416\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/99      1.48G    0.03925     0.0344    0.00766        156        640: 1\n",
      "tensor([1.00223], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.827      0.643      0.722      0.417\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/99      1.48G    0.03834    0.03398   0.007339        123        640: 1\n",
      "tensor([0.94780], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.839      0.631      0.725      0.425\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/99      1.48G    0.03806     0.0335   0.007081        174        640: 1\n",
      "tensor([1.16886], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.808      0.661      0.736      0.418\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/99      1.48G    0.03752    0.03288   0.006727        166        640: 1\n",
      "tensor([1.18900], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.836      0.667      0.746      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/99      1.48G     0.0372    0.03306   0.006557        152        640: 1\n",
      "tensor([1.00069], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.832      0.661       0.75      0.439\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/99      1.48G    0.03735    0.03317   0.006573        136        640: 1\n",
      "tensor([0.98454], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.823      0.687      0.756      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/99      1.48G    0.03701    0.03294   0.006336        134        640: 1\n",
      "tensor([0.98402], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.796      0.679       0.75      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/99      1.48G    0.03686    0.03248   0.006263        182        640: 1\n",
      "tensor([1.02493], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.834      0.682      0.758      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/99      1.48G    0.03663    0.03264   0.006091        128        640: 1\n",
      "tensor([0.86198], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.84      0.673      0.755      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/99      1.48G    0.03622    0.03216   0.006034        112        640: 1\n",
      "tensor([0.98715], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.842      0.695      0.775      0.468\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/99      1.48G    0.03605    0.03232   0.005877        151        640: 1\n",
      "tensor([0.91238], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.826      0.684      0.776      0.471\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/99      1.48G    0.03609    0.03241    0.00578        132        640: 1\n",
      "tensor([0.96744], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.842      0.702      0.778      0.476\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/99      1.48G    0.03595    0.03205   0.005782        131        640: 1\n",
      "tensor([0.89648], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.659      0.773      0.464\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/99      1.48G    0.03616    0.03211   0.005744        159        640: 1\n",
      "tensor([1.05359], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.831      0.699       0.78      0.471\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/99      1.48G    0.03575     0.0318   0.005607        125        640: 1\n",
      "tensor([0.89499], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.839      0.689      0.776      0.473\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/99      1.48G    0.03559    0.03199   0.005716         88        640: 1\n",
      "tensor([0.76188], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.845      0.701      0.785      0.481\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/99      1.48G    0.03521     0.0312   0.005555        137        640: 1\n",
      "tensor([1.11271], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.82       0.69      0.773      0.482\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/99      1.48G    0.03539    0.03192   0.005286        166        640: 1\n",
      "tensor([1.03875], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.685      0.785      0.492\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/99      1.48G    0.03508    0.03158   0.005399        161        640: 1\n",
      "tensor([1.01667], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.815      0.724       0.78      0.484\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/99      1.48G    0.03516    0.03124   0.005248        118        640: 1\n",
      "tensor([0.87003], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866      0.683       0.79      0.487\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/99      1.48G    0.03467    0.03123   0.005289        151        640: 1\n",
      "tensor([1.00554], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.827      0.695      0.777      0.482\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/99      1.48G    0.03486    0.03154   0.005231        133        640: 1\n",
      "tensor([0.90365], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856      0.682      0.781      0.491\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/99      1.48G    0.03465    0.03124    0.00519        154        640: 1\n",
      "tensor([1.04655], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865      0.702      0.797      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/99      1.48G     0.0348    0.03136   0.005257        122        640: 1\n",
      "tensor([0.84212], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.863       0.71      0.793      0.498\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/99      1.48G    0.03459    0.03122   0.004888        123        640: 1\n",
      "tensor([0.78066], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.707      0.798      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/99      1.48G    0.03441    0.03091   0.004961        127        640: 1\n",
      "tensor([0.77490], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.835      0.718      0.797      0.497\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/99      1.48G    0.03435    0.03001    0.00501        127        640: 1\n",
      "tensor([0.79969], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.852      0.698      0.795      0.497\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/99      1.48G    0.03415     0.0306   0.004953        122        640: 1\n",
      "tensor([0.90238], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88        0.7      0.798        0.5\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/99      1.48G    0.03424    0.03099   0.004834        146        640: 1\n",
      "tensor([0.95555], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.869      0.696      0.798      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/99      1.48G    0.03398    0.03042   0.004776        202        640: 1\n",
      "tensor([1.06846], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.863      0.713      0.803      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/99      1.48G    0.03398    0.03035   0.004811         94        640: 1\n",
      "tensor([0.68710], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88        0.7      0.806       0.51\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/99      1.48G    0.03396    0.03042   0.004773        152        640: 1\n",
      "tensor([1.03848], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.697        0.8      0.505\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/99      1.48G    0.03393    0.03038   0.004813        123        640: 1\n",
      "tensor([0.81109], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.873      0.723      0.806      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/99      1.48G    0.03365    0.03042   0.004876        162        640: 1\n",
      "tensor([0.89567], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856       0.74      0.808      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/99      1.48G    0.03387    0.03072   0.004799        161        640: 1\n",
      "tensor([0.89122], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.713      0.809      0.515\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/99      1.48G    0.03363    0.03055   0.004652        122        640: 1\n",
      "tensor([0.82606], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.887      0.701      0.803      0.509\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/99      1.48G    0.03361    0.03021   0.004742        126        640: 1\n",
      "tensor([0.77717], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.853      0.729      0.811      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/99      1.48G    0.03333    0.03017   0.004586         90        640: 1\n",
      "tensor([0.73145], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.703      0.808      0.512\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/99      1.48G     0.0333    0.03013   0.004542        118        640: 1\n",
      "tensor([0.88005], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.866      0.717       0.81      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/99      1.48G    0.03326    0.03019   0.004445        157        640: 1\n",
      "tensor([0.92269], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.858      0.737      0.817      0.521\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/99      1.48G    0.03323    0.03024   0.004358        104        640: 1\n",
      "tensor([0.68351], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.721       0.82      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/99      1.48G    0.03316    0.03055   0.004437        157        640: 1\n",
      "tensor([0.89625], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.732      0.817      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/99      1.48G    0.03313    0.02985   0.004294        108        640: 1\n",
      "tensor([0.72372], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.864       0.72      0.809      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/99      1.48G     0.0332    0.03006   0.004534        159        640: 1\n",
      "tensor([0.88579], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.846       0.74      0.808      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/99      1.48G    0.03274     0.0299   0.004344        118        640: 1\n",
      "tensor([0.87125], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867      0.719      0.809      0.511\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/99      1.48G     0.0329     0.0303   0.004383        176        640: 1\n",
      "tensor([1.02904], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87       0.73      0.822       0.53\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      50/99      1.48G    0.03305    0.02987   0.004467        130        640: 1\n",
      "tensor([0.80803], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.707      0.819      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      51/99      1.48G    0.03257    0.03007   0.004195        178        640: 1\n",
      "tensor([1.04938], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.856      0.728      0.811      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      52/99      1.48G    0.03256    0.02968   0.004253        148        640: 1\n",
      "tensor([0.80912], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.736      0.818      0.522\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      53/99      1.48G    0.03251     0.0296   0.004163        115        640: 1\n",
      "tensor([0.80526], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867       0.73      0.814      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      54/99      1.48G     0.0326    0.02946    0.00434        124        640: 1\n",
      "tensor([0.81111], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.858      0.752      0.821      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      55/99      1.48G    0.03231    0.02917   0.004269        163        640: 1\n",
      "tensor([0.85989], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.864      0.744      0.821      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      56/99      1.48G    0.03232    0.02957    0.00424        200        640: 1\n",
      "tensor([0.93989], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.849      0.737      0.822      0.533\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      57/99      1.48G    0.03229    0.02964   0.004178        141        640: 1\n",
      "tensor([0.80471], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.844      0.748      0.822      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      58/99      1.48G    0.03242    0.02959   0.004278        146        640: 1\n",
      "tensor([0.85297], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.713      0.813      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      59/99      1.48G    0.03235     0.0297   0.004273        168        640: 1\n",
      "tensor([0.88795], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.718      0.816      0.536\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      60/99      1.48G    0.03223    0.02934   0.004102        175        640: 1\n",
      "tensor([0.91496], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.726      0.825      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      61/99      1.48G    0.03193    0.02948   0.004142        139        640: 1\n",
      "tensor([0.92301], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.882      0.739      0.824      0.539\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      62/99      1.48G    0.03193    0.02863   0.004051        117        640: 1\n",
      "tensor([0.74844], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.867      0.756      0.825      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      63/99      1.48G    0.03219    0.02918   0.003899        129        640: 1\n",
      "tensor([0.79759], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.876      0.753      0.829      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      64/99      1.48G      0.032     0.0293    0.00409        109        640: 1\n",
      "tensor([0.74879], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.738      0.818      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      65/99      1.48G    0.03196    0.02941   0.003968        154        640: 1\n",
      "tensor([0.92587], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.869      0.731      0.815      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      66/99      1.48G    0.03158     0.0293   0.003965        119        640: 1\n",
      "tensor([0.82467], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.846      0.746      0.823      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      67/99      1.48G    0.03175    0.02864   0.004006        153        640: 1\n",
      "tensor([0.84371], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.901      0.722       0.83      0.543\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      68/99      1.48G    0.03176    0.02927   0.004052        116        640: 1\n",
      "tensor([0.77344], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.894      0.739      0.831      0.541\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      69/99      1.48G    0.03136     0.0285   0.004069        141        640: 1\n",
      "tensor([0.91108], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675        0.9      0.735      0.828      0.548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      70/99      1.48G    0.03157    0.02886   0.003922        175        640: 1\n",
      "tensor([1.00300], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.891      0.748      0.829      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      71/99      1.48G     0.0317    0.02903    0.00398        161        640: 1\n",
      "tensor([0.92262], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.751      0.834      0.548\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      72/99      1.48G    0.03148    0.02864   0.003881        114        640: 1\n",
      "tensor([0.82825], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.884      0.742      0.825      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      73/99      1.48G    0.03172     0.0292   0.004009        141        640: 1\n",
      "tensor([0.85054], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.889      0.746      0.835      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      74/99      1.48G    0.03153    0.02911   0.003755        133        640: 1\n",
      "tensor([0.75021], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.905      0.737       0.83      0.547\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      75/99      1.48G    0.03136    0.02863   0.003808        159        640: 1\n",
      "tensor([0.94157], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.739      0.827      0.546\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      76/99      1.48G    0.03149    0.02866   0.003927        122        640: 1\n",
      "tensor([0.72911], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.744       0.83      0.545\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      77/99      1.48G    0.03145    0.02908   0.003776        137        640: 1\n",
      "tensor([0.82082], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.881      0.742      0.838      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      78/99      1.48G    0.03143    0.02843   0.003786        137        640: 1\n",
      "tensor([0.79774], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.902      0.742      0.833       0.55\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      79/99      1.48G    0.03105    0.02821   0.003823        161        640: 1\n",
      "tensor([0.95601], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.885      0.738      0.832      0.549\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      80/99      1.48G    0.03114    0.02871   0.003652        154        640: 1\n",
      "tensor([0.80950], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.87      0.761      0.834      0.552\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      81/99      1.48G     0.0311     0.0284   0.003826        181        640: 1\n",
      "tensor([0.98569], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888       0.75      0.837      0.554\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      82/99      1.48G    0.03107    0.02833    0.00377        149        640: 1\n",
      "tensor([0.81880], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.893      0.748      0.835      0.553\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      83/99      1.48G    0.03086    0.02837     0.0036        118        640: 1\n",
      "tensor([0.84770], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.888      0.749       0.83      0.551\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      84/99      1.48G    0.03081     0.0284   0.003676        178        640: 1\n",
      "tensor([0.94665], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.873      0.756       0.83      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      85/99      1.48G    0.03078    0.02836   0.003612        140        640: 1\n",
      "tensor([0.87975], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.868      0.752      0.835      0.555\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      86/99      1.48G    0.03091    0.02846   0.003676        119        640: 1\n",
      "tensor([0.69689], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865      0.766      0.837      0.559\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      87/99      1.48G    0.03086    0.02865   0.003642        114        640: 1\n",
      "tensor([0.68086], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.883      0.763      0.834      0.557\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      88/99      1.48G    0.03095    0.02829   0.003657        117        640: 1\n",
      "tensor([0.73339], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.879      0.753      0.839      0.558\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      89/99      1.48G    0.03083    0.02847   0.003577        118        640: 1\n",
      "tensor([0.75499], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.865       0.76      0.835      0.559\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      90/99      1.48G    0.03068    0.02807   0.003508        115        640: 1\n",
      "tensor([0.72756], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.872      0.752      0.837      0.556\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      91/99      1.48G    0.03062    0.02786   0.003532        159        640: 1\n",
      "tensor([0.97130], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.895      0.749       0.84      0.564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      92/99      1.48G    0.03062    0.02822   0.003616        165        640: 1\n",
      "tensor([0.89406], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.878      0.758      0.837      0.562\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      93/99      1.48G    0.03046    0.02784   0.003509        126        640: 1\n",
      "tensor([0.75150], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.88      0.757      0.837       0.56\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      94/99      1.48G    0.03043    0.02795   0.003581        112        640: 1\n",
      "tensor([0.70960], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.886      0.752      0.836      0.562\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      95/99      1.48G    0.03024    0.02776   0.003416        121        640: 1\n",
      "tensor([0.72075], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.892      0.749      0.836      0.564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      96/99      1.48G    0.03061     0.0278   0.003566        195        640: 1\n",
      "tensor([0.82742], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675       0.89      0.749      0.838      0.564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      97/99      1.48G    0.03054    0.02795   0.003459        101        640: 1\n",
      "tensor([0.74052], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.871      0.765      0.838      0.566\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      98/99      1.48G    0.03035    0.02795   0.003571        137        640: 1\n",
      "tensor([0.74307], device='cuda:0', grad_fn=<MulBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       1048       5675      0.874      0.755      0.836      0.565\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      99/99      1.48G    0.03021    0.02799   0.003471        167        640:  "
     ]
    }
   ],
   "source": [
    "\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_SI.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_kitti.yaml \\\n",
    "--data data/kitti.yaml \\\n",
    "--epochs 100 \\\n",
    "--weights ./runs/train/fog_02/weights/best.pt \\\n",
    "--name not_freeze:{freeze_layer_str} \\\n",
    "--freeze {freeze_layer} \\\n",
    "\"\"\"\n",
    "!{command}\n",
    "# --weights ./runs/train/exp3/weights/best.pt \\\n",
    "# 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a1f2d4c7-5926-4174-b271-7cc54f14aaea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n",
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/not_freeze:0_1_2_3_4_5_6_7_8_9/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 160 layers, 7031707 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test... 2244 images, 0 bac\u001b[0m\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/test.cache\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.653      0.273      0.323      0.188\n",
      "                   Car       2244       8711      0.857      0.477      0.606       0.38\n",
      "                   Van       2244        861      0.696      0.285      0.341      0.236\n",
      "                 Truck       2244        333      0.509      0.156       0.17      0.109\n",
      "                  Tram       2244        138      0.522      0.174      0.201      0.113\n",
      "            Pedestrian       2244       1286       0.74      0.379      0.435      0.225\n",
      "        Person_sitting       2244         89       0.66      0.202      0.288       0.13\n",
      "               Cyclist       2244        496      0.702      0.218      0.256      0.145\n",
      "                  Misc       2244        284      0.539      0.292      0.288      0.167\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp340\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 然后是1.0雾测试集\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/test/* &&\\\n",
    "cp /root/autodl-tmp/datasets/fogged/fogged_strength1.0/* ../datasets/kitti/images/test/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n",
    "\n",
    "# 这是无雾测试集\n",
    "model = f'runs/train/not_freeze:0_1_2_3_4_5_6_7_8_9/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d818a03-8829-47da-98f5-08ce42f18bc9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8982e900-263d-43fa-a97f-af01bf41c6e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd17d6f9-4f3b-4055-ab0b-4ea3de27c6c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d421fdb7-1810-4ff1-8398-448e9eaea99d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set updated successfully!\n",
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti.yaml, weights=['runs/train/not_freeze:0_1_2_3_4_5_6_7_8_9/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 88456e3d Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_kitti summary: 160 layers, 7031707 parameters, 0 gradients, 15.8 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test... 2244 images, 0 bac\u001b[0m\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mNew cache created: /root/autodl-tmp/datasets/kitti/labels/test.cache\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.306      0.157      0.152     0.0817\n",
      "                   Car       2244       8711      0.762       0.37      0.443      0.259\n",
      "                   Van       2244        861      0.211      0.251      0.173      0.101\n",
      "                 Truck       2244        333      0.158     0.0691     0.0506     0.0328\n",
      "                  Tram       2244        138     0.0478    0.00725    0.00784    0.00278\n",
      "            Pedestrian       2244       1286      0.469       0.32      0.316      0.145\n",
      "        Person_sitting       2244         89      0.197     0.0196     0.0431     0.0108\n",
      "               Cyclist       2244        496      0.198     0.0927     0.0637     0.0329\n",
      "                  Misc       2244        284      0.403      0.127      0.121     0.0695\n",
      "Speed: 0.0ms pre-process, 0.9ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp341\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "#这个是使用ewc的增量训练在没有加雾的第一个数据集上的效果\n",
    "\n",
    "update_testsets = f\" \\\n",
    "rm ../datasets/kitti/images/test/* &&\\\n",
    "cp /root/autodl-tmp/testing/image_2/* ../datasets/kitti/images/test/ && \\\n",
    "echo 'Test set updated successfully!' \\\n",
    "\" \n",
    "!{update_testsets}\n",
    "\n",
    "\n",
    "model = f'runs/train/not_freeze:0_1_2_3_4_5_6_7_8_9/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "572f1ce0-73f2-47b6-9c09-b6e148140361",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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